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Abstract

The importance of leadership for key educational outcomes is well documented, yet leaders’ working conditions and well-being have received considerably less attention, particularly in the early care and education (ECE) sector. Job-Demands-Resources (JD-R) theory was used to develop a holistic conceptualization of leader well-being for the purpose of examining the associations among various job demands, resources, and well-being (i.e., physical, psychological, and professional well-being) for leaders of birth to 12th grade (B–12). We tested our conceptual model via structural equation modeling (SEM) with over 2,000 ECE and K–12 building leaders across the United States. We found substantially stronger effects between job demands and well-being relative to job resources and well-being, though resources were found to be directly associated with professional well-being. Further, our tested model was similar for both ECE and K–12 leaders. Overall findings suggest that a reduction in demands—not just increased resources—is needed to ensure improved leader well-being.

Keywords

early care and education early childhood leadership educational leader well-being educational leader working conditions health job demands-resources theory K–12 leadership structural equation modeling survey research

Educational leaders occupy a distinctive space in the landscape of American education. While they work among many others, they typically stand alone at their building sites, shouldering the lion’s share of responsibility for success—oftentimes with very little peer or supervisor support (Deutsch & Tong, 2011; Ford et al., 2020). They act as both a buffer and a liaison between the school and the outside world. Within their building, they have myriad responsibilities requiring a wide range of skills. They are simultaneously responsible for leading decision-making on issues related to curriculum and instruction; selecting, retaining, and evaluating teachers; addressing student behavior and fostering inclusive learning environments that optimize student growth and development; and managing the day-to-day operations and activities of their school sites (DeMatthews et al., 2021). Indeed, numerous studies tout the contributions of quality educational leadership to various as-pects of student, educator, and organizational improvement, development, and success (Douglass, 2018; Ford et al., 2019; Grissom et al., 2021; Hitt & Tucker, 2016).

Yet, with the potential to influence so much of what happens within a school building comes a sense of “ultimate responsibility” (Spillane & Lee, 2014). This feeling can breed unrealistic expectations and a selfless mindset (DeMatthews et al., 2021; Ray et al., 2020), which can be detrimental to well-being and ultimately decrease leader productivity (Mahfouz & Gordon, 2021). In the last two decades, we have gained a better understanding of the working conditions and well-being of K–12 school leaders, which are characterized by high accountability and demands, extensive workload, and limited control, as well as high stress, burnout, and secondary trauma (De-Matthews et al., 2019, 2023; Horwood et al., 2021; Mahfouz, 2018; Mahfouz & Gordon, 2021; Ray et al., 2020; Tikkanen et al., 2017; Wang et al. 2022; West et al., 2010). In the last few years, educational leaders have also had to contend with protracted COVID-19 pandemic conditions and intensifying political divisions (Kaufman et al., 2022; Urick et al., 2021). It is no wonder that they can experience their work as both challenging and stimulating as well as overwhelming and exhausting. Even before the start of the pandemic, K–12 principals reported experiencing more stress and burnout and less satisfaction in their jobs than in years past (DeMatthews et al., 2021). As a result, nearly one in five U.S. K–12 principals (18%) leave their school each year, which negatively impacts teaching and learning, particularly in schools with higher proportions of students of color (Kaufman et al., 2022; Taie & Lewis, 2023).

In comparison to research in the K–12 educational sector, much less is known about the nature of leadership in early care and education (ECE, referring to those who serve children from birth through age five; Douglass, 2017; Gibbs, 2022; Kirby et al., 2021; Muijs et al., 2004; Rodd, 2013)—a sector that has its own distinct origins and development within the U.S. educational landscape (Cahan, 1989; Lascarides & Hinitz, 2013). While ECE and K–12 leaders share similar administrative responsibilities, ECE leaders generally attend to a wider range of responsibilities beyond administration (e.g., covering classrooms, preparing food, doing laundry, driving a bus; Kirby et al., 2021). ECE leaders also have relatively fewer preservice and in-service professional development opportunities as compared to K–12 leaders (Douglass & Kirby, 2022). Given the differences between the two workforces, further investigation of ECE leaders’ well-being and working conditions, in addition to those of K–12 leaders, would contribute to the current literature on leader well-being in educational settings.

While a growing field of interest, educational leader well-being and working conditions have been outstripped by investment and interest in the working conditions and well-being of teachers, particularly during COVID (Kwon et al., 2021; Wilson et al., 2023). Yet leaders need someone to look out for them as they look out for teachers and children (DeMatthews et al., 2019; Urick et al., 2021), and the field has only begun to chart the landscape of support for educational leader and preservice leader well-being (Mahfouz, 2018, 2020; Mahfouz & Richardson, 2021; Ray et al., 2020; Urick et al., 2021). Such support would likely improve myriad leader outcomes such as motivation, well-being, and retention (DeMatthews et al., 2021; Doromal & Markowitz, 2023; Ford et al., 2020; Mahfouz, 2018). Given that it costs, on average, about $75k to replace a K–12 leader (The New Teacher Center, 2018), the economic implications alone of ensuring that leaders are thriving in their positions are considerable.

Furthermore, because of the influence that leaders have over school climate, working conditions, and the day-to-day experiences of those working in the school building, it is important to prioritize attention to leader needs as we recognize the critical role they play in supporting teachers (and students) in their growth, development, and well-being (Ford et al., 2019; Ford & Ware, 2018). Great leaders can provide support in the form of resources for teachers, work to reduce their demands, and strive to build a culture around self-care and healthy workplace practices. These, in turn, can reduce teacher attrition—a growing problem in both ECE and K–12 contexts (Carver-Thomas & Darling-Hammond, 2017; Doromal & Markowitz, 2023). Program or school administration that includes quality educational leadership and management practices is also critical for high classroom and instructional quality (Lower & Cassidy, 2007). Quality leadership and high expectations for staff and children predict the quality of students’ learning and experiences at ECE centers and schools (Ackerman, 2008; Rohacek et al., 2010) and are associated with the emotional well-being of teachers working with students (e.g., Ford et al., 2019; Mill & Romano-White, 1999).

Using a holistic conceptualization of leader well-being developed using the Job-Demands-Resources (JD-R) framework, the purpose of this study was to examine the associations among various job demands, resources, and their physical, psychological, and professional well- (and ill-) being. We investigated these associations not just for K–12 or ECE leaders but for leaders across both of these settings—what we refer to as leaders of birth to 12th grade (B–12). Specifically, our focus was on B–12 building leaders, as those persons with formal authority and responsibility for overseeing many (if not all) aspects of the administrative, financial, and instructional activities of their school or center (Douglass & Kirby, 2022).

We began by first formulating a conceptual framework of leader working conditions and well-being, which relied primarily on the extant literature on K–12 leadership. Then, we empirically tested our framework using a national sample of B–12 leaders in the United States. Below, we provide an integrated review of the literature on the job demands and resources of educational leaders, assuming that these two groups of leaders, at least from a JD-R perspective, may be more similar than different. We then explore the various ways in which leader well-being has been measured and its relationships to various leader working conditions.

Developing a Holistic Framework of B–12 Leader Working Conditions and Well-Being

The JD-R framework (also referred to as JD-R theory; Bakker & Demerouti, 2007) has been widely used in occupational studies across different disciplines around the world, including the teaching workforce. The JD-R-informed model we advance in this paper posits a set of direct and indirect paths from leader working conditions to well-being. This helps us test the appropriateness of a B–12 approach by combining prior JD-R applications in K–12 studies (e.g., Kaufman et al., 2022; Skaalvik, 2023) with conceptual models we have developed for ECE (Kwon et al., 2021; see Figure 1).

Figure 1.

Hypothesized model of the relationships between dimensions of working conditions and P3 leader well-being.

Two basic tenets of JD-R are (1) the categorization of all working conditions into either job demands or job resources (Bakker & Demerouti, 2017; Hakanen et al., 2008) and (2) that both job demands and resources activate unique and separate processes in individuals: a health impairment process (for demands) and a motivational process (for resources). Job demands refer to the various physical, psychological, social, or organizational aspects of the job that require sustained effort and are therefore associated with costs to one’s well-being (Bakker & Demerouti, 2017). Thus, JD-R theory posits that demands are primarily associated with physiological and psychological depletion (i.e., health impairment) that can lead to burnout, as well as a variety of other negative health and job-related outcomes (Bakker & Demerouti, 2017).

Conversely, job resources are defined as the various physical, psychological, social, or organizational aspects of the job that either reduce job demands, help individuals achieve their goals, or stimulate growth, learning, and development (Bakker & Demerouti, 2017). Unlike job demands, job resources precipitate a motivational process; engagement and interest are activated by meeting the basic psychological needs essential for human thriving (Bakker & Demerouti, 2007, 2017; Ryan & Deci, 2017). Furthermore, the JD-R model assumes shared perceptions and experiences and imposes a team or organizational approach, resulting in the individual being subsumed at the supra-individual level (Schaufeli & Taris, 2014).¹ As can be seen in Figure 1, our conceptual model posits a set of relations between cognitive, social-emotional, and organizational demands and resources and leader well-being, which includes three distinct dimensions we refer to as “P³” well-being: (a) professional well-being (job satisfaction, intent to leave, and isolation); (b) psychological well-being (depressive symptoms, hope, and emotional exhaustion); and (c) physical well-being (overall physical health, body mass index, exercise frequency, and doctor-diagnosed conditions).

Job Demands and Resources for B–12 Leaders

Educational leaders face a wide range of challenges in their roles and in the operation of their buildings. They must manage day-to-day operations, fiscal and human capital arrangements, and relationships with staff, among many others (U.S. Department of Labor, Bureau of Labor Statistics, 2014; Wishard et al., 2003). While they are consistently asked to provide support for their staff, children, and parents, they seldom receive support of their own (Maxfield et al., 2011). One area of educational leader demands that is well-documented is excessive workload, particularly since the accountability movement (DeMatthews et al., 2021; Ford et al., 2020; Wang et al., 2022). Time demands are an important aspect of leader work intensification in recent years, but these demands are also exacerbated by added work responsibilities, role conflict, and inadequate resources, leaving leaders feeling like there are not enough hours in the day for all that needs to be done (Collie et al., 2020; De Jong et al., 2017; Wang et al., 2022). Staff shortages and other human capital and financial concerns are frequently occurring demands for educational leaders (Kaufman et al., 2022; Skaalvik, 2023).

While seldom studied as an educational leader demand (see Yildirim & Sait Dinc, 2019, for an exception), role ambiguity is commonly understood as a leader demand in other occupational settings (Schmidt et al., 2014; Skaalvik, 2023). Role ambiguity, or lack of clarity or confusion about what is expected of you, is prevalent in educational settings because building leaders are a common point of connection between the school and its personnel, the community, and the district/agency that oversees them. Being at the nexus of many different constituencies can create confusion, particularly when conflicting messages from these various groups are received (Skaalvik, 2023). Finally, less considered in the leadership literature is how feelings of workplace discrimination might inflict significant demands on educators, particularly educators of color, as they navigate a complex work environment (Hall et al., 2023). Most of the research on workplace discrimination for educational leaders has been qualitative in nature (for a review, see Agosto & Roland, 2018), though this is a commonly considered demand in other occupational studies.

Because of their connection to motivation, in particular intrinsic motivation, the majority of job resources tend to follow the three core areas of basic psychological needs theory (Ryan & Deci, 2017)—competence, autonomy, and relatedness—and thus are fairly common across occupations. There are those resources that provide opportunities for personal and professional growth and the development of the skills needed in one’s job (competence), those that support the free exercise of competencies to meet job challenges (autonomy), and those that support the needs all individuals have for connection to one another in social systems (relatedness). In the context of leading centers/schools, organizational resources like coaching/feedback, mentoring, and professional learning opportunities provided by supervisors are critical (Collie et al., 2020; Skaavlik, 2023; Wong et al., 2024). Economic support in the form of salary or income is a common instrumental job resource as are various cognitive/emotional resources such as self-care supports, positive relationships with the school and outside community, and collegial support (Beausaert et al., 2023; Hu et al., 2016; Ray et al., 2020; Stephenson & Bauer, 2010).

Though there is little research in the area of ECE leader working conditions, there is little reason to believe that the challenges that ECE leaders face are substantially different from those of K–12 building leaders (Kirby et al., 2021). While sparse and mostly descriptive, the few studies in this area point to the following job demands: role complexity, characterized by simultaneous management of multiple roles (Muijs et al., 2004; Rodd, 2013); an overwhelming number of administrative tasks and role ambiguity in leading others (Kristiansen et al., 2021); limited job resources like social support (Kristiansen et al., 2021); and little professional development and preparation for the role (Douglass, 2018; Douglass & Kirby, 2022; Shore et al., 2021).

Job Demands and Resources: Links to the Well-Being of B–12 Leaders

What constitutes well-being is the subject of considerable, ongoing debate. For this study, we adopted a definition of leader well-being rooted in the idea of healthy, engaged human functioning brought about by the fulfillment of basic human psychological needs (Ryan & Deci, 2017). In the context of leading a school building, this refers to leaders’ evaluations of healthy functioning in their work environment that are manifest via a wide range of feelings, attitudes, and behaviors (Van Horn et al., 2004). While the term well-being suggests that it is constituted primarily of positive feelings and assessments, the literature is replete with the use of both positive and negative indicators of well-being in occupational studies (Bakker & Demerouti, 2017; Collie, 2021; Collie et al., 2015; Ryan & Deci, 2017). Our approach incorporates both positive and negative discernments as this is more consistent with JD-R theory, where working conditions (demands and resources) have been found to be uniquely and independently related to employee well-being (Bakker & Demerouti, 2018).

There has been an increasing interest in educational leaders’ well-being, and prior studies point to the interrelated way that it is associated with various job demands and resources (Berkovich & Eyal, 2015; Chen et al., 2021; Maxwell & Riley, 2016). Educational leaders need to constantly manage the competing demands of various stakeholders, attend to their emotional needs and well-being, and maintain cooperative and harmonious relationships with them, which exacts significant psychological and professional effort and risk (Berkovich & Eyal, 2015). Thus, leaders often experience high levels of stress and feel overwhelmed by their many responsibilities (McCormick Center for Early Childhood Leadership, 2003; Wang et al., 2022). Coping with such demands is necessarily emotionally and psychologically depleting and, if chronic, can lead to burnout, job dissatisfaction, and a desire to leave the profession (DeMatthews et al., 2021; Kaufman et al., 2022; Skaalvik, 2023; Yildirim & Sait Dinc, 2019). Furthermore, many educational leaders do not feel well prepared for their administrative roles (e.g., only 27% of ECE directors perceived themselves as prepared for their administrative work; McCormick Center for Early Childhood Leadership, 2003), which can attenuate feelings of efficacy essential for job engagement and role longevity (Federici & Skaalvik, 2011, 2012). Lack of preparation for the role can, in turn, lead to confusion about one’s responsibilities (i.e., role ambiguity/conflict); one meta-analysis of studies across many occupational sectors found a sizeable link between role ambiguity and depression (Schmidt et al., 2014).

Because they do not have a peer group in their immediate work environment, building leaders as professionals are also more likely to struggle with feelings of loneliness and isolation as they cope with difficult demands (Mahfouz, 2018, 2020), particularly if they have little supervisory support (DeMatthews et al., 2021). Improved social connections can serve as a buffer to increasing job demands and professional well-being (Bauer et al., 2019; Grubb & Flessa, 2006; Mahfouz, 2020). Support from colleagues outside of the school, supervisor support and collaboration, and trust in management are also specific factors that predict general and mental health across time (Beausaert et al., 2023). Relatedly, a lack of social support can lead to leaders’ emotional and physical burnout (Stephenson & Bauer, 2010).

Likewise, participatory climate as a resource has emerged as a critical factor for occupational commitment under conditions of high staff shortages—a pattern that was observed across all 22 countries in TALIS 2013 (Collie et al., 2020). Similarly, Skaalvik (2023) found that job satisfaction and emotional exhaustion fully mediated the relationships between school principals’ job demands and resources and motivation to quit. Other studies have pointed to the nuanced way job resources and job demand factors relate to negative outcomes. For example, Kaufman et al. (2022) found that although principals’ reported resource needs and teacher shortages were consistently related to dissatisfaction and intention to leave, various other job demands were predictors of dissatisfaction (e.g., multiple job demands, budget issues, cost-cutting measures, teacher vacancies) but not intent to leave.

There is very limited literature on ECE leader well-being in relationship to job demands and resources, with Zheng et al. (2022) and Wong and colleagues (2024) as notable exceptions. Zheng et al. found that emotional job demands can actually enhance principals’ emotional regulation, meaning that emotional job demands provide leaders with the opportunity to adapt and adopt strategies to regulate their emotions. Trust in colleagues served as an interpersonal resource for reappraisal—an emotional regulation strategy that is particularly effective in the long term. Finally, caring for the emotional demands of early childhood leadership mediated the influence of work characteristics on job satisfaction and emotional exhaustion. Wong and colleagues (2024) found that clinical supervision of center directors helped to improve overall job satisfaction and reduce the sense of isolation they felt in coping with demanding working conditions.

As indicated in our conceptual model, we hypothesize that job demands and resources are directly associated with leaders’ professional well-being. We also expected that job demands and resources would be indirectly associated with leaders’ professional well-being through their associations with physical and psychological well-being, and leaders’ P³ well-being would also be associated with one another. Given the unique histories and characteristics of both the ECE and K–12 sectors (Cahan, 1989; Lascarides & Hinitz, 2013; Markowitz & Bassok, 2024), another important dimension to this analysis was whether or not these hypothesized relationships/pathways were similar across or unique to each sector.

Method

The Happy Leader Project research team collected national data on ECE and K—12 building leaders’ P³ well-being and working conditions, including demands, resources, and support. After obtaining approval from the University Institutional Review Board, invitations to participate in the study were sent to the e-mails of school leaders compiled from available state databases as well as those of private data firms. The sample frame generated from these combined lists consisted of 139,658 leaders across both ECE and K–12 sectors with valid e-mail addresses. Interested leaders completed a consent form and a questionnaire (i.e., background, well-being, working conditions) on their own time. Qualtrics was used to organize and administer the 48-question survey, which included a comprehensive set of measures aligned with our conceptual framework (discussed later). The survey required an average of 30 minutes to complete, and 50 participating leaders out of the over 2000 respondents were randomly chosen to receive a small incentive ($50) in the form of a gift card for their participation.

The survey, which was administered at the end of the 2021–2022 school year (May to June 2022), yielded 2,281 valid survey responses from eligible participants represe-nting all 50 states and the District of Columbia. These responses include 1,462 identifying as K–12 leaders in a variety of public, private, and charter schools (64% of sample) and 819 identifying as early childhood education leaders in a variety of for-profit and non-profit child care and Head Start/Early Head Start centers (36% of sample). For ECE leaders, we did not include family childcare homes given their unique characteristics (e.g., they are often only a care provider in the setting). School leaders eligible for this study were those serving formal leadership positions as a “building leader” (not a district or other entity) in K–12 public, private, or charter schools or ECE public, private, or Head Start/Early Head Start facilities. This resulted in a variety of titles/positions being eligible—namely, principals or center directors, associate directors/assistant principals, headmasters, executive directors, and owners.

Sample demographics are provided in Table 1. Leaders’ ages ranged from 23 to 84 years (M = 50.52, SD = 9.33). Participating leaders were predominately female (76%) but almost exclusively female in ECE (97%) and more mixed in K–12 settings (66%). In terms of racial and ethnic background, building leaders in our sample were almost 83% Caucasian, 8.3% African American, 1.3% Native American, 4.6% Hispanic, 1.4% Asian/Pacific Islander, and 1.5% reporting a multiracial or biracial identity. Because of the different requirements/pathways of those in ECE settings versus K–12 settings, the educational backgrounds of participants were varied. App-roximately 10% of the sample held an associate’s degree (including a child development associate’s) or less, in-cluding about 3% who didn’t hold any degree. App-roximately 16% had a bachelor’s and approximately 75% had a master’s degree or higher.

Table 1

Characteristics of Birth to 12th-Grade Building Leaders in ECE and K–12 Settings (n = 2040)

Categories	Percentage OR Mean (Range OR SD)
Leader Characteristics
Female	76.0%
Leaders with master’s degree or higher	74.9%
Leaders by School/Center Type
ECE: Childcare center (nonprofit)	18.5%
ECE: Childcare center (for-profit)	10.9%
ECE: Head Start/Early Head Start	4.9%
K–12: Public school (traditional)	46.4%
K–12: Public school (charter)	4.9%
K–12: Private school (religious)	10.0%
K–12: Private school (nonsectarian)	3.9%
K–12 Full-time virtual school (charter, district, or private)	0.6%
Leader Race
Asian or Pacific Islander	1.4%
Black or African American	8.3%
Hispanic or Latinx	4.6%
Multiracial or biracial	1.5%
Native American or Alaskan Native	1.3%
White or Caucasian	82.8%
Leader age (in years)	50.52
Prior Experience
As classroom teacher (number of years)	11.24 (7.02)
As educational leader (number of years)	10.45 (8.60)
Annual salary (median)	$85,000
School/Center Characteristics
Rural center/school	32.4%
Suburban center/school	41.5%
Urban center/school	26.1%
Free/reduced lunch (percent of students in center/school)	44.9%

These sample demographics are largely on par with what we know of the K–12 and ECE leadership sectors, though there is little data available nationally on the ECE context (Markowitz & Bassok, 2024), including with respect to racial/gender composition of ECE leadership. Recent data indicates that approximately 20% of pre-K–12 public school principals are leaders of color and are about 55% female (National Education Association [NEA], 2019). For ECE directors, aggregated data from nine states demonstrate that 95% are female and around 20% are leaders of color (Abel et al., 2018). In the absence of more comprehensive national data on ECE leadership, we know that the ECE teaching workforce is almost exclusively female (97%, Coffey, 2022); it is likely that the same is true of the representation of females in ECE leadership, given the limited pathways to leadership in ECE (Douglass, 2017).

Ninety-three percent of participants held either the title of school/center director (23.4%), principal (54.9%), head of school/headmaster (3.4%), or executive director (11.6%). The remaining leaders were either associate center directors (1.7%), assistant principals (2.3%), childcare owners, or held other leadership positions (2.7%). The remaining 7% of the sample held multiple titles of those listed previously. The number of years that the participating leaders have worked as a leader ranged from less than a year to 52 years (M = 10.45 years, SD = 8.60). The median wage for all leaders in the sample was $85,000.

Measures

Following is a brief description of the key measures used in the present study. More detailed descriptions and information on the origins and psychometric properties of each measure are reported in Table 2.

Table 2

Description of Measures Used in the Study

Construct	Study variables	Instruments	Instrument characteristics	Psychometric properties
Job demands	Workload Role ambiguity/conflict Workplace discrimination Financial/human resource needs	Job Satisfaction Survey (Spector, 1985) Copenhagen Psychosocial Questionnaire III (Pejtersen et al., 2010). Workplace Prejudice/Discrimination Inventory (James et al., 1994) Questionnaire	Four items adapted from the JSS ask participants about their workload, paperwork, hours, and level of attention required in their job. Participants were asked to rate how true each statement of their work was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Three items adapted from the COPSOQ-III ask participants about clarity of expectations, frequency of interruptions, and the compatibility of policies, guidelines, and requests. Participants were asked to rate how true each statement of their work was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Two items adapted from the WPDI ask participants about instances of discrimination at their workplace and unfair treatment due to either their sex, race, disability, etc. Participants were asked to rate how true each statement of their workplace was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Three separate, summed items to capture total financial, material, and personnel resources needed to carry out their work on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree).	α = .755 α = .775 α = .688 n/a
Job resources	Opportunities for professional growth Coaching/mentoring Communication Self-care supports	Early Childhood Work Environment Survey – Short Version (Jorde-Bloom, 1988) Questionnaire Early Childhood Work Environment Survey – Short Version (Jorde-Bloom, 1988) Questionnaire	Four items adapted from the ECWES that ask participants about access to higher education opportunities and promotion prospects. Participants were asked to rate how true each statement of their work was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Three items that ask about school leaders receiving coaching/mentoring support, feedback, and professional development targeted toward specific leadership skills. Participants were asked to rate how true each statement of their work was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Three items adapted from the ECWES ask participants about how clear and open communication is regarding their performance and daily operations from supervisor(s). Participants were asked to rate how true each statement of their workplace was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree). Eight items (1 = yes, 0 = no) that ask school leaders to indicate all types of support for their own self-care they receive, including resources and training on self-care practices, time off for medical/counseling appointments or mental health days, moral support from supervisors, access to employee assistance programs, or others.	α = .669 α = .701 α = .753 n/a
	District support of basic psychological needs	District/network support for leaders (Elseman, 2021; Olsen, 2017)	Nine-item measure of supervisor support based on the three psychological needs of self-determination theory (competence, autonomy, and relatedness). My supervisor provides feedback, celebrates growth, instills confidence, listens to my opinions, explains their rationale, cares, trusts me to make decisions, and is trustworthy. Participants were asked to rate how true each statement of their workplace was on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree).	α = .950
Physical well-being	General healthOverall physical healthBody mass index	QuestionnaireSF-12 Health Survey (PCS subscale; Ware et al., 1996)Adult BMI calculator (Centers for Disease Control & Prevention, 2022)	Eleven-item measure that asks respondents to select health conditions (1 = yes, 0 = no) that a doctor has diagnosed them with within the last year or at any point in their life, including heart disease, high blood pressure, high cholesterol, diabetes, asthma, urinary tract infection, migraines, etc., or other).Six-item measure that asks respondents to answer questions about their perceptions of their own physical health and in what ways their physical health interferes with their day-to-day lives.Respondents provide their self-reported height (e.g., 5′5″) and weight (e.g., 165 lbs). BMI = 703 x [weight (lbs) / (height (in)²)]	n/aα = .733n/a
	Exercise	Self-care practices of school principals (Ray et al., 2020; Centers for Disease Control & Prevention, 2022)	One item asking respondents to indicate how many days per week (0–7) that they engage in moderate to vigorous physical activities (heavy lifting, running, digging, aerobic dancing, or bicycling uphill).	n/a
Psychological well-being	Depressive symptomsEmotional exhaustion	Shortened version of the Center for Epidemiologic Studies Depression Scale-Revised (Andresen et al., 1994)Maslach Burnout Inventory (MBI-ES) (Maslach et al., 1996)	Ten-item screening test asking respondents to reflect on the previous week and rate the frequency of symptoms on a scale of 0 (not at all or less than 1 day) to 3 (5–7 days). Symptoms include items, such as “I had trouble keeping my mind on what I was doing,” “I felt fearful,” and “I felt lonely.” Scores equal to or above 10 is considered to indicate high risk for depression.Adapted 4-item Emotional Exhaustion subscale of the Maslach Burnout Inventory that asks participants about feelings of being emotionally overextended and exhausted by their work on a scale of 0 (never) to 6 (every day). Permission to reproduce the MBI-ES for survey administration was given under license from Mind Garden, Inc.	CES-D-10; Cronbach α = .65–.91; (Baron et al., 2017; Bjorgvinsson et al., 2013)Current study: α = .750α = .910
	Dispositional hope	Adult Hope Scale (Snyder et al., 1991)	12-item measure that asks participants about their pathway thinking and agency thinking. Participants were asked to rate how true each statement was on an 8-point scale: 1 (definitely false) to 8 (definitely true).	α = .855
Professional well-being	Job satisfactionIntent to leaveIsolation	TALIS 2018 Principal Survey (Organization for Economic Co-operation and Development, 2018)Organizational Commitment Questionnaire (Meyer et al., 1993)UCLA Loneliness Scale for work settings (Dussault & Thibodeau, 1997; Izgar, 2009; Russell et al., 1978, 1980)	6 items adapted from the OECD’s TALIS 2018 principal survey that asks participants about their feelings toward their current school, job, and chosen profession. Participants were asked to rate how true each statement was of their work on a 5-point scale: 1 (strongly disagree) to 5 (strongly agree).4 items from the OCQ that asks participants to indicate how likely it is that they would leave their school or the teaching profession in the next year on a scale of 1 (definitely not) to 6 (definitely).10-item measure that asks participants about their feelings of loneliness and social isolation on a 4-point scale: 1 (never) to 4 (often)	α = .810α =.833Internal consistency (coefficient a ranging from .89 to .94) and test-retest reliability over a 1-year period (r = .73).Current study, α= .884

Working Conditions

In alignment with our conceptual framework, working conditions for educational leaders were divided into two categories: job demands and job resources. We used a combination of various existing measures and a modified version of a new comprehensive working condition measure developed for ECE teachers (Kwon et al., under review).

Job demands

Principals and childcare center leaders were asked about their job demands in terms of (1) workload, which includes question about the amount of paperwork, hours worked beyond expectations, frequency of interruptions, and amount of work relative to time; (2) role ambiguity, which addresses the conflict between supervisor requests, policies and guidelines and mixed messages or lack of clarity around work expectations; and (3) financial/human capital needs, which measures the degree to which leaders feel that they have the money, materials, equipment, and personnel to do their job. Finally, workplace discrimination, which captured leaders’ experiences of discrimination with aspects of their identity such as race, gender identity, disability, and religion.

Job resources

In terms of job resources, leaders were asked to reflect on their access to: (1) coaching/mentorship opportunities, including feedback on performance and specific leadership-oriented professional development; (2) opportunities for professional growth, including promotion pathways and support for degree attainment; (3) basic psychological needs support from supervisors, in the areas of competence (i.e., instills confidence in my ability to do my job well), autonomy (i.e., trusts me to address problems how I see fit), and relatedness (cares about me as a person/I feel like I am part of a team); (4) self-care supports, which asked respondents to note which of the self-care resources they had access to (e.g., resources on self-care, time for appointments/counseling, mental health days, employee assistance programs, etc.); and (5) clear and open communication from supervisors.

P³ Well-Being

Well-being for educational leaders was divided into three categories: physical well-being, psychological well-being, and professional well-being. Each latent construct included both measures of well-being and ill-being.

Physical well-being

To evaluate leaders’ physical well-being, we measured the overall physical health of educational leaders using the SF-12 (PCS subscale; Ware et al., 1996) and asked respondents about their exercise behaviors (Ray et al., 2020). For physical ill-being, we also captured self-reported height and weight to calculate body mass index (Centers for Disease Control and Prevention, 2022) and doctor-diagnosed and other health-related symptoms/risk factors, such as diabetes, heart disease, asthma, high blood pressure, urinary tract infections, and infectious diseases (Kwon et al., 2021). Self-report of physical well-being can be suspect due to social desirability bias; studies have demonstrated that estimates of height are usually within 0.1-inch accuracy; self-report of weight is typically underestimated by 4.6 pounds (Brunner Huber, 2007).

Psychological well-being

The psychological well-being of B–12 leaders was operationalized as dispositional hope (Adult Hope Scale; Snyder et al., 1991); conversely, we included two measures of ill-being: depressive symptoms (Center for Epidemiologic Studies of Depression Short Form (CES-D-10) and emotional exhaustion (MBI-ES; Maslach et al., 1996).

Professional well-being

The three dimensions of professional well-being were measured. Those were job satisfaction (Organization for Economic Co-operation and Development, 2018) for professional well-being and intent to leave (Meyer et al., 1993) and isolation (UCLA Loneliness Scale for work settings; Dussault & Thibodeau, 1997; Izgar, 2009; Russell et al., 1978, 1980) as measures of professional ill-being. Descriptive statistics for the working conditions and well-being variables in our study are presented in Table 3.

Table 3

Descriptive Statistics on Well-Being and Working Conditions of Birth–12th-Grade Leaders

Categories (Min/Max of Measure)	Mean (SD) or %
Psychological Well-Being
Depressive symptoms (0–30)	8.34 (5.60)
Depressive symptoms (Clinical levels ≥ 10)	37.5%
Dispositional hope (8–64)	54.01 (6.24)
Emotional exhaustion (0–6)	2.80 (1.63)
Physical Well-Being/Health
Body mass index	30.1(6.73)
Exercise (number of days/week)	2.07(1.73)
Doctor diagnosed conditions (in the past year; 0–11)	0.78(1.23)
Overall physical health (SF-12, PCS; 6-26)	16.28 (2.57)
Professional Well-being
Isolation (1–4)	1.94 (0.61)
Job satisfaction (1–5)	4.22 (0.74)
Intent to leave (1–5)	2.42 (1.21)
Job Demands and Resources
Demands: Workload (1–5)	4.41 (0.59)
Demands: Role ambiguity/incongruence (1–5)	2.44 (1.02)
Demands: Workplace discrimination (1–5)	1.97 (1.08)
Demands: Financial and human capital needs (3–15)	8.01 (2.71)
Resources: Professional growth opportunities (1–5)	3.32 (0.86)
Resources: Communication quality (1–5)	3.81 (0.87)
Resources: Mentoring/coaching (3–18)	10.39 (3.56)
Resources: Self-care supports (0–7)	2.85 (1.63)
Resources: Basic psychological need supports (1–5)	3.97 (0.96)

Analytical Approach

To test our conceptual model of leader working conditions and well-being, we employed a structural equation modeling (SEM) approach via AMOS 28.0 using maximum likelihood estimation. We reported bootstrapped regression coefficients, standard errors, and confidence intervals in this analysis, with a chosen resample size of n = 1000. Missing data in our sample on our working conditions and well-being measures were very small and limited to random item nonresponse; they were well under 1% for all measures with the exception of exercise, which was around 8%. For bootstrapping, complete data on all measures was needed; thus, listwise deletion of any missing cases was necessary and reduced our final analytic sample to 2,040 building leaders (34% ECE, 66% K–12). In this case, a sensitivity analysis was performed via a series of Bonferroni corrected t-tests of unequal variance between those who reported their exercise habits and those who did not on all measures. No significant differences were found.

In testing our model, we considered the following covariates as potential controls: (a) ECE/K–12 leader status, (b) high-poverty school/center status (>60% of children below poverty line/free-or-reduced lunch), (c) leader age and gender, and (d) educational level and income. Of these statistical controls, none improved model fit and the complexity of the model and number of parameters estimated precluded retaining them in the final model. To further test the differences between ECE/K–12 leader status, we split the sample and tested the structural model separately for each to determine whether the tested relationships differed in structure depending on leader type.

SEM construction began by first conducting confirmatory factor analyses (CFAs) of all candidate latent measures of demands, resources, and well-being. We examined both first-order and second-order configurations of our latent measures of demands and resources since these constructs were hypothesized to each have subdimensions (see Figure 1). In all cases, first-order factor structures for demands and resources were superior in fit. Our criteria for including latent measures in our final SEM was based upon theory and factor loadings given that three-item (or less) CFAs are just-identified and therefore provide no fit statistics. Factor loadings for all variables included in the measurement model needed to be at or above 0.35 (Costello & Osborne, 2005). Once our latent measure structure was established, our overall modeling approach was primarily driven by our conceptual framework and prior research but also included an empirical component for the purposes of testing direct paths as well as refining and settling on a final model. We first tested our hypothesized model (noted in Figure 1) with all possible paths entered between these constructs. From here, we employed a model trimming approach (Kline, 2015) based upon the results of the saturated model where we eliminated nonsignificant paths step-by-step and examined our path estimates, as well as absolute and incremental fit statistics retaining those paths that both were theoretically justified and that improved model fit. The loadings of only positive well-being constructs were constrained to 1, thus making the overall latent well-being measures positive even though they were comprised of measures of both well-being and ill-being.

Results

In assessing SEM fit, we chose both measures of absolute (e.g., chi-square, AIC, RMSEA) and incremental fit (e.g., CFI, TLI). Established guidelines suggest that TLI and CFI statistics of 0.95 or higher indicate good model fit, with greater than .90 being adequate; similarly, an RMSEA of less than or equal 0.06 indicates a good model fit (Hu & Bentler, 1999). A nonsignificant chi-square value indicates good overall fit; however, because it is well known to be sensitive to sample size (typically leading to a rejected null), we relied more heavily on our other chosen indices in assessing model fit. Figure 2 and Table 4 display the results of our overall final model of B–12 leader working conditions and P³ well-being. The overall fit for our model was acceptable, $χ^{2}$ = 1286.59, df = 144, p < .001, CFI = .902, RMSEA = .062, 90% CI [.059, .066], with the various job demands and resources as well as well-being measures explaining 78% of the variance in professional well-being (which consisted of job isolation, satisfaction, and intent to leave), 60% in psychological well-being (i.e., hope, emotional exhaustion, and depression), and 10% in physical well-being (BMI, overall physical health, exercise, and doctor-diagnosed conditions). As mentioned previously, none of our selected controls when added to the model were statistically significant, nor did they improve model fit, so they were excluded.

Figure 2.

Empirical test of B–12 leader well-being and working conditions (n = 2040).

Table 4

Direct and Indirect Effects, and Covariances for SEM of B–12 Leader Well-Being and Working Conditions

Path (Direct Effects)	B	β	SE(β)	p	95%CI(β)
Psychological Well-Being ON
Job demands	–6.632	–0.622	0.023	.002	[–0.667, –0.581]
Physical bell-being	0.371	0.309	0.029	.002	[0.252, 0.365]
Physical Well-Being ON
Job demands	–3.728	–0.420	0.058	.001	[–0.547, –0.321]
Job resources	–0.174	–0.156	0.053	.003	[–0.275, –0.057]
Professional Well-Being ON
Job resources	0.089	0.292	0.025	.001	[0.244, 0.347]
Psychological well-being	0.171	0.758	0.030	.002	[0.696, 0.818]
Physical well-being	–0.039	–0.144	0.029	.002	[–0.200, –0.084]
Path (Indirect Effects)
Professional Well-Being ON
Job demands	–1.229	–0.510	0.023	.002	[–0.553, –0.463]
Job resources	–0.004	–0.014	0.006	.002	[–0.029, –0.005]
Physical well-being	0.064	0.234	0.026	.002	[0.185, 0.288]
Psychological Well-Being ON
Job resources	–0.065	–0.048	0.016	.003	[–0.085, –0.018]
Job demands	–1.382	–0.130	0.019	.001	[–0.174, –0.098]
Covariances
Job resources WITH
Job demands		–0.713	0.028	.001	[–0.768, –0.658]

Note. Bootstrapped unstandardized and standardized coefficients/estimates from AMOS reported (resampling size = 1000). Bias-corrected standard errors and 95% confidence intervals included.

The final, recursive model generally supported our hypothesized model of leader well-being and working conditions. It revealed several paths of note from leader demands and resources to professional well-being via psychological and physical well-being. Beginning with leader job demands, the model demonstrated moderate-to-strong, negative total effects on professional well-being, B = −1.229, β = −0.510, SE(β) = 0.022, p < .01, 95% CI(β) [−0.553, −0.463], psychological well-being, B = −8.015, β = −0.752, SE(β) = 0.023, p < .01, 95% CI(β) [−0.796, −0.704], and physical well-being, B = −3.728, β = −0.420, SE(β) = 0.057, p < .01, 95% CI(β) [−0.547,−0.321]. In contrast, the total effects of job resources on all P³ well-being were comparatively much smaller: professional well-being, B = 0.084, β = 0.278, SE(β) = 0.027, p < .01, 95% CI(β) [0.228, 0.332], psychological well-being, B = −0.065, β = −0.048, SE(β) = 0.017, p < .01, 95% CI(β) [−0.085, −0.018], and physical well-being, B = −0.174, β = −0.156, SE(β) = 0.055, p < .01, 95% CI(β) [−0.275,−0.057].

Breaking these total effects down into direct and indirect components, we found no direct effects of job demands on professional well-being, though there was a direct effect for job resources on professional well-being, B = 0.089, β = 0.292, SE(β) = 0.025, p < .01, 95% CI(β) [0.244, 0.347]. Regarding direct effects on what we hypothesized were mediating well-being conditions—namely, physical and psychological well-being—job demands had sizeable, negative direct effects on both physical well-being, B = -3.728, β = −0.420, SE(β) = 0.058, p < .01, 95% CI(β) [−0.547, −0.321] and psychological well-being, B = −6.632 β = −0.622, SE(β) = 0.023, p < .01, 95% CI(β) [−0.667, −0.581]. In addition to the direct effect of job resources on professional well-being mentioned earlier, there was also a small, negative association of job resources with physical well-being, B = −0.174, β = −0.156, SE(β) = 0.053, p < .01, 95% CI(β) [−0.275, −0.057]. This, along with a small, negative direct effect between physical well-being and professional well-being, resulted in a positive indirect effect of job resources on well-being through physical well-being (β = 0.022).

Total indirect effects were in expected directions, with physical well-being having a positive association with professional well-being via psychological well-being, B = 0.064, β = 0.234, SE(β) = 0.026, p < .01, 95% CI(β) [0.185, 0.288]. Notable in this path is the direction of the relationship between physical and psychological well-being, B = 0.371, β = 0.309, SE(β) = 0.029, p < .01, 95% CI(β) [0.252, 0.365], which pointed from the physical to the psychological. In our conceptual model, we did not posit a specific direction for this relationship, and although models with both possibilities were similar in fit, the path with psychological well-being as endogenous was marginally superior. Finally, the indirect effect of job demands on professional well-being via physical and psychological well-being were substantial, B = −1.229, β = −0.510, SE(β) = 0.023, p < .01, 95% CI(β) [−0.553, −0.463].

Because there is very little research on the well-being and working conditions of ECE building leaders, we did not know whether to expect the models of working conditions and well-being to be the same or similar as those for K–12 building leaders. Thus, our final test was to split our file into the two constituent groups and build and test these two models independently of one another. Our final model results for K–12 and ECE building leaders were the same in terms of paths and significant effects but fit slightly better for ECE leaders than K–12: ECE leaders: $χ^{2}$ = 511.43, df = 144, p < .001, CFI = .904, RMSEA = .060, 90% CI [.055, .066]; K–12 leaders: $χ^{2}$ = 932.25, df = 144, p < .001, CFI = .898, RMSEA = .064, 90% CI [.060, .068]. Thus, there were no modifications to our final model that resulted in a better model fit for K–12 leaders.

Discussion

In this study, we tested the appropriateness of a B–12 model to conceptualize both ECE and K–12 building leaders’ well-being and working conditions; our findings generally support such an approach, suggesting that leaders across sectors share similar working conditions, well-being, and the relationships between them. However, further research in support of this claim is still needed. We also tested the associations among ECE and K–12 leaders’ physical, professional, and psychological well-being with attention to their association with various job demands and resources. In doing so, this study is one of the few studies to examine multiple dimensions of leader well-being (and ill-being) simultaneously, including physical well-being, which is understudied across all educator groups in both the K–12 and ECE sectors (Ford et al., 2020; Kwon et al., 2021; Randall et al., 2023). Ironically, most studies of educator well-being predominantly use measures of poor well-being or ill-being, and, in our study, we attempted to take a more balanced approach by triangulating each domain of well-being using both positive and negative indicators and doing so for both those building leaders serving K–12 schools as well as ECE centers.

Our descriptive data showed that although the educational leaders in this study have high levels of hope and moderate-to-high levels of job satisfaction, many of them exhibited less-than-optimal levels of well-being. For example, a substantial proportion of leaders reported clinically high depressive symptoms (37.5%) and moderate levels of emotional exhaustion. They also reported experiencing a high workload with, on average, 10 hours/week beyond normal work hours. Seventy-eight percent of leaders in our sample were overweight or obese and exercised around 2 days a week or less on average. Very little data exists on the physical well-being of leaders (both in K–12 and ECE), with the exception of Ray et al. (2020) and O’Neill and Glasson (2019). Ray et al.’s finding that the majority of K–12 principals in Arkansas reported exercising 2 or fewer days per week is remarkably consistent with our finding of a little over 2 days per week on average. Furthermore, these findings are strikingly similar to studies conducted on the physical well-being and health behaviors of teachers in ECE contexts (Kwon et al., 2021; Lessard et al., 2020; Randall et al., 2023).

The data we present on high job demands, workload, and poor psychological and professional well-being of B–12 leaders corroborate many of the findings of previous studies (DeMatthews et al., 2019, 2023; Horwood et al., 2021; Mahfouz, 2018; Mahfouz & Gordon, 2021; Ray et al., 2020; Tikkanen et al., 2017; Wang et al., 2022; West et al., 2010). As we hypothesized, there were significant direct and indirect associations among job demands, job resources, and various aspects of ECE and K–12 leader well-being. Specifically, there were direct associations of job demands with leaders’ poor physical and psychological well-being, while job resources were directly associated with physical and professional well-being. First, these findings are consistent with JD-R theory (Bakker & Demerouti, 2007) and past comprehensive studies of whole educator well-being (Kwon et al., 2021), which document two distinct mechanisms: one in which job demands precipitate health impairment processes and one in which job resources engender motivational processes that are associated with improved worker (i.e., leader) well-being (Beausaert et al., 2023; Stephenson & Bauer, 2010).

Turning to specific relationships, demands had the strongest direct relationship with psychological well- (and/or ill-) being (as measured by hope, emotional exhaustion, and depressive symptoms), a finding that is mirrored across many K–12 and ECE educator workforce studies (Collie, 2021; Collie et al., 2020; DeMatthews et al., 2021; Kwon et al., 2021; Tikkanen et al., 2017; Wang et al., 2022; Yildirim & Sait Dinc, 2019). Our findings add these same patterns of associations for educational leaders. Further, these demands were indirectly associated with professional well- (and/or ill-) being (as measured by job satisfaction, isolation, and intent to leave). These findings, too, are consistent with prior studies (Collie et al., 2020; Kaufman et al., 2022). Until now, however, most studies have only examined direct associations of demands and resources to either psychological or professional well-being. One of the unique contributions of this study was to examine the relationship of both in tandem with demands and resources, exploring their potential as mediating conditions.

Interestingly, resources were not as strongly associated with P³ well-being, calling into question the sustainability of policy and program initiatives only aimed at providing resources for leaders to combat increasing demands. While programs aimed at mindfulness, and other self-care activities for educators are increasing in number and quality (see, for example, Mahfouz, 2018; Mahfouz & Gordon, 2021; Ray et al., 2020), these programs will not alone address the endemic challenges of a profession that has become overburdened with responsibility and charged with doing more with less. These findings suggest that while it is important to provide additional resources, working to reduce job demands might be even more critical to improving the well-being of leaders who work under an increasingly untenable set of job demands.

Regarding indirect associations between job demands and resources and professional well-being, one interesting dimension of the findings was the indirect relationship among resources and professional well-being, which was flanked by negative associations with physical well-being. There are two potential explanations for this finding. One is that these findings could simply be an artifact of the overall poor physical well-being of leaders in our sample: over 78% of our leader sample had BMIs that were in the overweight to obese range and had an average moderate-to-vigorous exercise frequency of just under two days per week. It is also possible that, while resources are framed within JD-R theory as inducing motivation via the provision of needed psychological support to grow and thrive, resources related to professional growth nevertheless can exact costs on well-being. In other words, because professional growth resources likely require additional work commitment, they can displace time spent on other activities such as exercise, sleep, or other physical health improvement activities.

It is also noteworthy to mention that various well-being dimensions were significantly associated with one another. In particular, leaders’ physical well-being was directly and indirectly associated with professional well-being via its link to psychological well-being. This finding suggests that ECE and K–12 leaders’ physical well-being is an important predictor and mediator for their professional well-being as measured by job satisfaction, feelings of isolation, and intent to leave. Although the findings on indirect effects were consistent with one particular study that tested a similar model with ECE teachers (Kwon et al., 2021), the present study also found a direct effect of physical well-being on professional well-being that was not present in a similar model of teachers. Relatedly, the low explained variance in physical well-being could be because, unlike teachers (in particular, ECE teachers) who have high physical demands due to lifting, bending, and stooping with children (Randall et al., 2023), physical demands might not be as prevalent a concern for the working conditions of building leaders—as such, they were not examined as closely in this study.

As mentioned earlier in the discussion, the overall comparison of total effects between job demands and job resources demonstrated that job demands were a far stronger overall predictor of P³ well-being for B–12 leaders than job resources and this includes their indirect effects on professional well-being. This finding is particularly interesting, given that one proposition of JD-R theory is the “boosting” effects of resources on well-being when high demands are present (Bakker & Demerouti, 2017). While we did not directly examine this “boosting” mechanism, our model suggests that increased resources in our measured domains would not be sufficient to combat the high demands of leaders. Future research should seek to examine this boosting phenomenon in greater detail.

Implications for Policy and Practice

Overall, our findings suggest that it may not be more resources that are needed to improve leader well-being but rather reduced demands or a combination of both reduced job demands and increased job resources. In recent years, adding resources has been a favored policy solution to the problem of workplace well-being; in organizations like schools where leaders are compelled to do “more with less,” reducing demands never appears to be an option. Yet, if we are truly serious about improving educator well-being, we must recognize the limitations of expanding resources without reducing demands. Research on the plight of educational leaders is clear: they are overextended, overwhelmed, and exhausted (DeMatthews et al., 2019, 2021; Ray et al., 2020; Yildirim & Sait Dinc, 2019). Our findings suggest that one particular area where demands could be reduced is in the area of workload, where leaders clearly have more to do than they have time to do it, and this includes paperwork as well as interruptions.

This said, our findings suggest that resources, too, have demonstrable, direct benefits for all B–12 school leaders. When supervisors can provide space and opportunity for all leaders to grow into their role, give them valuable coaching and feedback, provide them with clear communication as well as psychological support and encouragement, leaders are likely to experience higher job satisfaction as well as reduced feelings of isolation and turnover intention—a finding echoed in a very recent study of ECE center directors (Wong et al., 2024). Finally, finding that our model of working conditions and well-being did not vary significantly by K–12 or ECE leader status is important because it suggests that policy recommendations related to improving these conditions might be most helpful conceptualized as B–12 policy recommendations, though understandably what these changes look like in policy and practice might differ according to the contexts unique to ECE versus K–12 educational leadership.

Limitations

Few studies, regardless of merits, are without limitations. Some important limitations of this study are also endemic to observational, survey-based studies—namely the cross-sectional nature of the study and the reliance of our study on the self-report of leader working conditions and well-being. In light of our findings regarding the poor overall physical and psychological health and challenging working conditions of B–12 leaders, it is likely that any self-report bias, were it to exist in this study, would likely underestimate, not overestimate, ill-being.

Though our study was national in scope, we were only able to reach a relatively small number of all available B–12 educational leaders in the United States. Due to a lack of national data and only sporadic data at the state level on ECE leaders in particular (Markowitz & Bassok, 2024), it is difficult to ascertain whether or not our sample is truly representative of the national ECE context; however, it is important to note that we make no such claim of representativeness. As such, any claims as to the generalizability of our findings to the U.S. ECE or K–12 leadership workforce writ large should be exercised with caution.

The work of educational leaders is complex, with many extant job demands and resources. Part of our approach was to be comprehensive in representing these facets of the work, based upon prior literature. Nevertheless, we do acknowledge that, although we considered many different measures of demands and resources, there may have been others related to the work of K–12 or ECE leaders whose inclusion in this study might have precipitated larger model divergences between these two unique groups of building leaders. One area for deeper inquiry in future studies will be on capturing the physical demands and resources of B–12 leaders, which continues to be an underexamined aspect of building leader experience.

Conclusion

Leading any human improvement enterprise is difficult, challenging, and taxing work. Our holistic and interdisciplinary approach to educational leader well-being offers a multifaceted view of leader working conditions, health, and well-being with the ultimate goal of understanding and supporting these educators, giving them the tools to more effectively support the learning and social growth of both teachers and children. Modifying and adopting measures from other fields allowed us to assess and understand neglected aspects of educational leader well-being (i.e., physical well-being, proximal psychological well-being conditions) in understanding what predicts more frequently studied conditions such as leader intent to leave, satisfaction, and burnout. Our findings highlight the importance of new policies and practices focused on removing leader demands that can be implemented alongside current initiatives to boost resources for school leaders. Our overall goal in this study was to encourage a sustained line of inquiry into the work, health, and well-being of all building leaders, with particular attention to those in ECE settings whose experiences have been woefully understudied.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for publication was provided by the University of Oklahoma and Utah State University Libraries’ Open Access Funds and the School of Teacher Education and Leadership at Utah State University. The Happy Leader Project would also like to acknowledge generous seed funding for this research from the University of Oklahoma Jeannine Rainbolt College of Education.

Open Practices Statement

The data and analysis files for this article can be found at: https://www.openicpsr.org/openicpsr/project/198107/version/V1/view OR

ORCID iDs

Timothy G. Ford

Kyong-Ah Kwon

Notes

Authors

TIMOTHY G. FORD is an associate professor of educational leadership and policy studies at the University of Oklahoma. His research focus centers on the motivation, working conditions, and well-being of birth–12 teachers and leaders.

KYONG-AH KWON is Drusa B. Cable Chair and professor of early childhood education at the University of Oklahoma. She is a former early childhood teacher interested in supporting young children’s early experiences and the early childhood workforce.

ALYSON L. LAVIGNE is an associate professor of instructional leadership at Utah State University. Using her training as an educational psychologist and classroom researcher, Lavigne has conducted research on teacher retention, teachers’ beliefs, teacher supervision and evaluation, and culturally and linguistically diverse minoritized students’ experiences.

TOM MCHUGH is student success coach at the University of Tulsa. He is interested in the role of leadership and post-secondary institutions in student retention, persistence, and mental health issues.

References

Abel

M. B.

Talan

Magid

(2018). Closing the leadership gap: 2018 status report on early childhood program leadership in the United States. McCormick Center for Early Childhood Leadership.

Ackerman

D. J.

(2008). Coaching as part of a pilot quality rating scale initiative: Challenges to—and supports for—the change-making process. Early Childhood Research & Practice, 10(2). http://ecrp.uiuc.edu/v10n2/ackerman.html

Agosto

Roland

(2018). Intersectionality and educational leadership: A critical review. Review of Research in Education, 42(1), 255–285.

Andresen

E. M.

Malmgren

J. A.

Carter

W. B.

Patrick

D. L.

(1994). Screening for depression in well older adults: Evaluation of a short form of the CES-D. American Journal of Preventive Medicine, 10(2), 77–84.

Bakker

A. B.

Demerouti

(2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22, 309–328.

Bakker

A. B.

Demerouti

(2017). Job Demands-Resources theory: Taking stock and looking forward. Journal of Occu-pational Health Psychology, 22, 273–285. http://dx.doi.org/10.1037/ocp0000056

Bakker

A. B.

Demerouti

(2018). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In Diener

Oishi

Tay

(Eds.), Handbook of well-being (pp. 593–604). DEF Publishers.

Baron

E. C.

Davies

Lund

(2017). Validation of the 10-item centre for epidemiological studies depression scale (CES-D-10) in Zulu, Xhosa and Afrikaans populations in South Africa. BMC Psychiatry, 17, 1–14.

Bauer

S. C.

Silver

Schwartzer

(2019). The impact of isolation on new principals’ persistence: Evidence from a southern US state. Educational Management Administration & Leadership, 47(3), 383–399.

10.

Beausaert

Froehlich

D. E.

Riley

Gallant

(2023). What about school principals’ well-being? The role of social capital. Educational Management Administration & Leadership, 51(2), 405–421.

11.

Berkovich

Eyal

(2015). Educational leaders and emotions: An international review of empirical evidence 1992–2012. Review of Educational Research, 85(1), 129–167. https://doi.org/10.3102/0034654314550046

12.

Björgvinsson

Kertz

S. J.

Bigda-Peyton

J. S.

McCoy

K. L.

Aderka

I. M.

(2013). Psychometric properties of the CES-D-10 in a psychiatric sample. Assessment, 20(4), 429–436.

13.

Brunner Huber

L. R

. (2007). Validity of self-reported height and weight in women of reproductive age. Maternal and Child Health Journal, 11, 137–144.

14.

Cahan

E. D.

(1989). Past caring: A history of US preschool care and education for the poor, 1820–1965. National Center for Children in Poverty.

15.

Carver-Thomas

Darling-Hammond

(2017). Teacher turnover: Why it matters and what we can do about it. Learning Policy Institute. https://doi.org/10.54300/454.278

16.

Centers for Disease Control and Prevention. (2022, September 2). Adult BMI calculator. https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/bmi_calculator.html

17.

Chen

Berkovich

Eyal

(2021). School leaders’ emotional experiences and capabilities: Perspectives, challenges, and prospects. Asia-Pacific Education Researcher, 30, 311–313. https://doi.org/10.1007/s40299-021-00564-y

18.

Coffey

(2022, July 19). Still underpaid and unequal: Early childhood educators face low pay and a worsening wage gap. Center for American Progress. https://www.americanprogress.org/article/still-underpaid-and-unequal/

19.

Collie

R. J.

(2021). A multilevel examination of teachers’ occupational commitment: The roles of job resources and disruptive student behavior. Social Psychology of Education, 24(2), 387–411.

20.

Collie

R. J.

Granziera

Martin

A. J.

(2020). School principals’ workplace well-being: A multination examination of their job resources and job demands. Journal of Educational Administration, 58(4), 417–433.

21.

Collie

R. J.

Shapka

J. D.

Perry

N. E.

Martin

A. J.

(2015). Teacher well-being: Exploring its components and a practice-oriented scale. Journal of Psychoeducational Assessment, 33(8), 744–756.

22.

Costello

A. B.

Osborne

J. W.

(2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation, 10(7), 1–9.

23.

De Jong

Grundmeyer

Yankey

. (2017). Identifying and addressing themes of job dissatisfaction for secondary principals. School Leadership & Management, 37(4), 354–371.

24.

DeMatthews

Carrola

Reyes

Knight

(2021). School leadership burnout and job-related stress: Recommendations for district administrators and principals. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 94(4), 159–167.

25.

DeMatthews

D. E.

Carrola

Knight

Izquierdo

(2019). Principal burnout: How urban school leaders experience secondary trauma on the US-Mexico border. Leadership and Policy in Schools, 18(4), 681–700.

26.

DeMatthews

D. E.

Reyes

Carrola

Edwards

James

(2023). Novice principal burnout: Exploring secondary trauma, working conditions, and coping strategies in an urban district. Leadership and Policy in Schools, 22(1), 181–199.

27.

Demerouti

Bakker

A. B.

Xanthopoulou

(2019). Job demands-resources theory and the role of individual cognitive and behavioral strategies. In Taris

Peeters

De Witte

(Eds.), The fun and frustration of modern working life: Contributions from an occupational health psychology perspective (pp. 94–104). Pelckmans Pro.

28.

Deutsch

F. M.

Tong

T. L.

(2011). Work-to-school mentoring: Childcare center directors and teachers’ return to school, Mentoring & Tutoring: Partnership in Learning, 19, 157–177, https://doi.org/10.1080/13611267.2011.564349

29.

Doromal

J. B.

Markowitz

A. J.

(2023). Following the leader: Associations between leader support and teacher retention in child care settings. Early Childhood Research Quarterly, 65, 250–260.

30.

Douglass

(2017). Leading for change in early care and education: Cultivating leadership from within. Teachers College Press.

31.

Douglass

(2018). Redefining leadership: Lessons from an early education leadership development initiative. Early Childhood Education Journal, 46(4), 387–396.

32.

Douglass

Kirby

(2022). Evaluating leadership development in early care and education. Early Childhood Research Brief, OPRE Report 2022-141. Office of Planning, Research, and Evaluation, Administration for Children & Families., U.S. Department of Health and Human Services.

33.

Dussault

Thibodeau

(1997). Professional isolation and performance at work of school principals. Journal of School Leadership, 7(5), 521–536.

34.

Elseman

(2021). District support for principal learning and development instrument. Doctoral dissertation, University of Oklahoma, Tulsa, Oklahoma.

35.

Federici

R. A.

Skaalvik

E. M.

(2011). Principal self-efficacy and work engagement: Assessing a Norwegian principal self-efficacy scale. Social Psychology of Education, 14, 575–600.

36.

Federici

R. A.

Skaalvik

E. M.

(2012). Principal self-efficacy: Relations with burnout, job satisfaction and motivation to quit. Social Psychology of Education, 15, 295–320.

37.

Ford

T. G.

Lavigne

Fiegener

(2020). Understanding district support for leader development and success in the accountability era: A review of the literature using social-cognitive theories of motivation. Review of Educational Research, 90(2), 264–307.

38.

Ford

T. G.

Olsen

J. J.

Khojasteh

Ware

J. K.

Urick

(2019). Effects of leader support for teacher psychological needs on teacher burnout, commitment, and intent to leave. Journal of Educational Administration, 57(6), 615–634.

39.

Ford

T. G.

Ware

J. K.

(2018). Teacher Self-Regulatory Climate (TSRC): Conceptualizing an indicator of leader support for teachers’ learning and development. Leadership and Policy in Schools, 17(1), 27–51.

40.

Gibbs

(2022). Leadership emergence and development: Organizations shaping leading in early childhood education. Educational Management Administration, and Leadership, 50(4), 672–693.

41.

Grissom

J. A.

Egalite

A. J.

Lindsay

C. A.

(2021). What great principals really do. Educational Leadership, 78(7), 21–25.

42.

Grubb

W. N.

Flessa

J. J.

(2006). A job too big for one: multiple principals and other nontraditional approaches to school leadership. Educational Administration Quarterly, 42(4), 518–550.

43.

Hakanen

J. J.

Schaufeli

W. B.

Ahola

(2008). The Job Demands-Resources model: A three-year cross-lagged study of burnout, depression, commitment, and work engagement. Work & Stress, 22(3), 224–241.

44.

Hall

Bassok

Doromal

J. B.

Markowitz

A. J.

(2023). A director like me: Teacher-leader racial/ethnic match and job outcomes in child care centers. Early Childhood Research Quarterly, 62, 369–384.

45.

Hitt

D. H.

Tucker

P. D.

(2016). Systematic review of key leader practices found to influence student achievement: A unified framework. Review of Educational Research, 86(2), 531–569.

46.

Horwood

Marsh

H. W.

Parker

P. D.

Riley

Guo

Dicke

(2021). Burning passion, burning out: The passionate school principal, burnout, job satisfaction, and extending the dualistic model of passion. Journal of Educational Psychology, 113(8), 1668–1688.

47.

C-M.

Cui

S-J.

Wang

(2016). Path analysis and work family conflict, job salary and promotion satisfaction, work engagement to subjective well-being of the primary and middle school principals. Journal of Education and Training Studies, 4(9), 10–15.

48.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

49.

Izgar

(2009). An investigation of depression and loneliness among school principals. Educational Sciences: Theory and Practice, 9, 247–258.

50.

James

Lovato

Cropanzano

(1994). Correlational and known-group comparison validation of a workplace prejudice/discrimination inventory. Journal of Applied Social Psychology, 24(17), 1573–1592.

51.

Jorde-Bloom

(1988, Summer). Assess climate of center-Use of ECWE survey. Day Care and Early Education, 15(4), 9–11. https://doi.org/10.1007/BF02361666

52.

Kaufman

J. H.

Diliberti

M. K.

Hamilton

L. S.

(2022). How principals’ perceived resource needs and job demands are related to their dissatisfaction and intention to leave their schools during the COVID-19 pandemic. AERA Open, 8(1), 1–18. https://doi.org/10.1177%2F23328584221081234

53.

Kirby

Douglass

Lyskawa

Jones

Malone

(2021). Understanding leadership in early care and education: A literature review. OPRE Report 2021-02. Office of Planning, Research, and Evaluation, Administration for Children & Families., U.S. Department of Health and Human Services.

54.

Kline

R. B.

(2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

55.

Kristiansen

Tholin

K. R.

Bøe

(2021). Early childhood centre directors coping with stress: Firefighters and oracles. International Journal of Educational Management, 35(4), 909–921.

56.

Kwon

Ford

T. G.

Jeon

Malek-Lasater

A. D.

Ellis

N. D.

Randall

Kile

M. S.

Salvatore

(2021). Testing a holistic conceptual framework for early childhood teacher well-being. Journal of School Psychology, 86, 178–197.

57.

Kwon

Jang

Ford

T. G.

Chappel

. (under review). Validating a comprehensive measure of early care and education (ECE) teacher working conditions.

58.

Lascarides

V. C.

Hinitz

B. F.

(2013). History of early childhood education. Routledge.

59.

Lessard

L. M.

Wilkins

Rose-Malm

Mazzocchi

M. C.

(2020). The health status of the early care and education workforce in the USA: A scoping review of the evidence and current practice. Public Health Reviews, 41, 1–17.

60.

Lower

J. K.

Cassidy

D. J.

(2007). Child care work environments: The relationship with learning environments. Journal of Research in Childhood Education, 22, 189−204.

61.

Mahfouz

(2018). Mindfulness training for school administrators: Effects on well-being and leadership. Journal of Educational Administration, 56(6), 602–619.

62.

Mahfouz

(2020). Principals and stress: Few coping strategies for abundant stressors. Educational Management Administration & Leadership, 48(3), 440–458.

63.

Mahfouz

Gordon

D. P.

(2021). The case for focusing on school principals’ social–emotional competencies. Management in Education, 35(4), 189–193.

64.

Mahfouz

Richardson

J. W.

(2021). At the crossroads: Wellbeing and principalship preparation. Journal of Research on Leadership Education, 16(4), 360–384.

65.

Markowitz

Bassok

(2024). The early care and education workforce in the United States. In Scott

Youngs

Cohen-Vogel

(Eds.), AERA handbook on education policy (2nd ed.). Routledge.

66.

Maslach

Jackson

S. E.

Leiter

M. P.

(1996). Maslach burnout inventory manual. Consulting Psychologists Press.

67.

Maxfield

C. R.

Ricks-Doneen

Klocko

B. A.

Sturges

(2011). Developing and supporting early childhood teacher leaders: A leadership project connecting university, community and public school resources. The International Journal of Educational Leadership Preparation, 6(1), 1–10.

68.

Maxwell

Riley

(2016). Emotional demands, emotional labour and occupational outcomes in school principals: Modeling the relationships, Educational Management Administration & Leadership, 45(3), 484–502. https://doi.org/10.1177/1741143215607878

69.

McCormick Center for Early Childhood Leadership. (2003). Lead-ership transitions: What do directors experience? McCormick Center for Early Childhood Leadership Publications. https://digitalcommons.nl.edu/mccormickcenter-pubs/46

70.

Meyer

J. P.

Allen

N. J.

Smith

C. A.

(1993). Commitment to organizations and occupations: Extension and test of a three-component conceptualization. Journal of Applied Psychology, 78(4), 538–551.

71.

Mill

Romano-White

(1999). Correlates of affectionate and angry behavior in child care educators of preschool-aged children. Early Childhood Quarterly, 14, 155–178.

72.

Muijs

Aubrey

Harris

Briggs

(2004). How do they manage? A review of the research on leadership in early childhood. Journal of Early Childhood Research, 2(2), 157–169.

73.

National Education Association [NEA]. (2019, June). Data Brief: Diversity and Representation in PK-12 Education Administration (Report No.: NBI 2018-111). Center for Enterprise Strategy.

74.

O’Neill

M. R.

Glasson

(2019). Revitalising professional learning for experienced principals: Energy versus ennui. Educational Management Administration & Leadership, 47(6), 887–908.

75.

Olsen

(2017). Principal support of teacher psychological needs: The conceptualization and measurement of a new construct. Doctoral dissertation, University of Oklahoma.

76.

Organization for Economic Co-operation and Development [OECD]. (2018). Teaching and Learning International Survey (TALIS): Principal Questionnaire. http://www.oecd.org/education/school/TALIS-2018-MS-Principal-Questionnaire-ENG.pdf.

77.

Pejtersen

J. H.

Kristensen

T. S.

Borg

Bjorner

J. B.

(2010). The second version of the Copenhagen Psychosocial Questionnaire. Scandinavian Journal of Public Health, 38, 8–24. https://doi.org/10.1177/1403494809349858.

78.

Randall

Kwon

Ford

T. G.

Malek-Lasater

(2023). Correlates of physical well-being in early childhood teachers: Aspects of fitness and work-related musculoskeletal issues for these “Educational Athletes.” Early Education and Development, 34(2), 551–571.

79.

Ray

Pijanowski

Lasater

(2020). The self-care practices of school principals. Journal of Educational Administration, 58(4), 435–451.

80.

Rodd

(2013). Leadership in early childhood (4th ed.). McGraw-Hill Education (UK).

81.

Rohacek

Adams

G. C.

Kisker

E. E.

(2010). Understand quality in context: Child care centers, communities, markets, and public policy. Washington D.C. Urban Institute

82.

Russell

Peplau

L. A.

Cutrona

C. E.

(1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39(3), 472–480.

83.

Russell

Peplau

L. A.

Ferguson

M. L.

(1978). Developing a measure of loneliness. Journal of Personality Assessment, 42(3), 290–294.

84.

Ryan

R. M.

Deci

E. L.

(2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.

85.

Schaufeli

W. B.

Taris

T. W.

(2014). A critical review of the job demands-resources model: Implications for improving work and health. In Bauer

G. F.

Hämmig

(Eds.), Bridging occupational, organizational and public health: A transdisciplinary approach (pp. 43–68). Springer.

86.

Schmidt

Roesler

Kusserow

Rau

(2014). Uncertainty in the workplace: Examining role ambiguity and role conflict, and their link to depression—a meta-analysis. European Journal of Work and Organizational Psychology, 23(1), 91–106.

87.

Shore

R. A.

Lambert

R. G.

Shue

P. L.

(2021). An evaluation of leadership professional development for early childhood directors. Leadership and Policy in Schools, 20(4), 690–703.

88.

Skaavlik

(2023). Emotional exhaustion and job satisfaction among Norwegian school principals: Relations with perceived job demands and job resources. International Journal of Leadership in Education, 26(1), 75–99.

89.

Snyder

C. R.

Harris

Anderson

J. R.

Holleran

S. A.

Irving

L. M.

Sigmon

S. T.

, et al. (1991). The will and the ways: Development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60(4), 570–585. https://doi.apa.org/doi/10.1037/0022-3514.60.4.570

90.

Spector

P. E.

(1985). Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey. American Journal of Community Psychology, 13, 693–713.

91.

Spillane

J. P.

Lee

L. C.

(2014). Novice school principals’ sense of ultimate responsibility: Problems of practice in transitioning to the principal’s office. Educational Administration Quarterly, 50(3), 431–465.

92.

Stephenson

L. E.

Bauer

S. C.

(2010). The role of isolation in predicting new principals’ burnout. International Journal of Education Policy and Leadership, 5(9), 1–17.

93.

Taie

Lewis

(2023). Principal attrition and mobility: Results from the 2021 -22 principal follow-up survey to the National Teacher and Principal Survey (NCES 2023-046). U.S. Department of Education, National Center for Education Statistics. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2023046

94.

The New Teacher Center [NTC]. (2018). Churn: The high cost of principal turnover. https://newteachercenter.org/wp-content/uploads/2021/07/Churn-The-High-Cost-of-Principal-Turnover_RB21.pdf

95.

Tikkanen

Pyhältö

Pietarinen

Soini

(2017). Interrelations between principals’ risk of burnout profiles and proactive self-regulation strategies. Social Psychology of Education, 20, 259–274.

96.

U.S. Department of Labor, Bureau of Labor Statistics. (2014, Jan. 8). Occupational outlook handbook, 2014-15. https://www.bls.gov/opub/mlr/2014/

97.

Urick

Carpenter

B. W.

Eckert

(2021). Confronting COVID: Crisis leadership, turbulence, and self-care. Frontiers in Education, 6, Article No. 642861, 1–11.

98.

Van Horn

J. E.

Taris

T. W.

Schaufeli

W. B.

Schreurs

P. J

. (2004). The structure of occupational well-being: A study among Dutch teachers. Journal of Occupational and Organizational Psychology, 77(3), 365–375.

99.

Wang

Pollock

Hauseman

(2022). Time demands and emotionally draining situations amid work intensification of school principals. Educational Administration Quarterly, 59(1), 112–142.

100.

Ware

Jr. Kosinski

Keller

S. D.

(1996). A 12-Item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220–233.

101.

West

D. L.

Peck

Reitzug

U. C.

(2010). Limited control and relentless accountability: Examining historical cha-nges in urban school principal pressure. Journal of School Leadership, 20(2), 238–266. https://doi.org/10.1177/105268461002000206

102.

Wilson

Plesko

Brockie

T. N.

Glass

(2023). The well-being of Head Start teachers: A scoping literature review. Journal of Early Childhood Teacher Education, 44(4), 747–772.

103.

Wishard

Shivers

E. M.

Howes

Ritchie

(2003). Child care program and teacher practices: Associations with quality and children’s experiences. Early Childhood Research Quarterly, 18, 65–103. https://doi.org/10.1016/S0885-2006(03)00007-3

104.

Wong

Bull

Cumming

McFarland

(2024). Supporting leaders in early childhood education settings: An evaluation of a clinical supervision program for centre directors. Cogent Education, 11(1), Article No. 2309749, 1–14. https://doi.org/10.1080/2331186X.2024.2309749

105.

Yildirim

Sait Dinc

(2019). Factors influencing burnout of the principals: A pilot study in Flemish schools of Belgium. Economic Research, 32(1), 3538–3553.

106.

Zheng

Dan

Luo

. (2022). A job demands-resources perspective on Kindergarten principals’ occupational well-being: The role of emotional regulation. International Journal of Environmental Research and Public Health, 19, 1–15.

Testing a Holistic Framework of Early Care and Education and K–12 Leaders’ Working Conditions and Well-Being

Abstract

Keywords

Developing a Holistic Framework of B–12 Leader Working Conditions and Well-Being

Job Demands and Resources for B–12 Leaders

Job Demands and Resources: Links to the Well-Being of B–12 Leaders

Method

Measures

Working Conditions

Job demands

Job resources

P3 Well-Being

Physical well-being

Psychological well-being

Professional well-being

Analytical Approach

Results

Discussion

Implications for Policy and Practice

Limitations

Conclusion

Footnotes

Funding

Open Practices Statement

ORCID iDs

Notes

Authors

References

P³ Well-Being