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Abstract

Objectives:

This study investigates burnout and subjective well-being among the Maternal and Child Health (MCH) workforce, considering recent events such as the Dobbs decision, the maternal mortality crisis, and the COVID-19 pandemic.

Methods:

An anonymous web-based, cross-sectional survey was conducted among 313 MCH professionals in the United States. Data were collected using validated measures, including the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) and the Oxford Happiness Questionnaire (OHQ) short scale. Sociodemographic characteristics and factors associated with burnout and subjective well-being were examined using univariate statistics and multivariable models.

Results:

Analysis revealed moderate levels of burnout among MCH professionals, particularly in emotional exhaustion. However, subjective well-being levels were relatively high. After controlling for covariates, significant associations were found between subjective well-being and burnout dimensions, as well as sociodemographic factors such as sex and race.

Conclusions:

The study’s findings indicate that higher subjective well-being is significantly associated with lower burnout, emotional exhaustion, and higher personal accomplishment. Variations in burnout and well-being are also influenced by sociodemographic factors such as age, sex, race, and occupation. Tailored interventions addressing the specific needs of MCH professionals are essential for building a resilient workforce. Organizational reforms and legislative measures are crucial for fostering supportive workplace environments and ensuring access to care and services amidst workforce challenges.

Keywords

burnout subjective well-being maternal and child health workforce development resilience

Introduction

The study of burnout within the healthcare sector originated in the late 1960s to describe the emotional and psychological strain experienced by clinical staff working with socially disadvantaged patients in free clinics.¹ Seminal research defines burnout as a combination of emotional fatigue, depersonalization, and a diminished sense of personal achievement resulting from persistent stress in the medical field.² On the other hand, subjective well-being is often used to measure happiness and is associated with resilience, an essential protective factor against burnout.^3-5

Rather than being categorized as a medical condition in the 11th Revision of the International Classification of Disease (ICD-11), burnout is considered an occupational phenomenon.⁶ Nevertheless, several medical conditions are associated with burnout, including sleep disorders, infections, depression, and anxiety.^7,8 Furthermore, burnout alone can impact an individual’s health and well-being and carry profound implications for turnover rates, absenteeism, and quality of care within certain professions.^9-12 Burnout can also pose safety risks for the people and communities served by professions with high burnout rates, such as healthcare providers and the public health workforce.¹²

Several factors make healthcare providers and the public health workforce susceptible to burnout. Professionals in these fields face long hours and heavy workloads and are exposed to sensitive issues in their interactions with patients and clients.^13-15 Furthermore, workers who are often the targets of harassment and feel they lack adequate resources and control to perform their jobs are at high risk of burnout.^16,17 These job characteristics typically associated with healthcare and public health have been exacerbated by the global pandemic, making professionals in this field highly susceptible to burnout.¹⁸

In the public health workforce, burnout is higher among employees with 1 to 9 years of work experience, and high levels of burnout are also observed among employees with 10 to 14 years of experience. Additionally, burnout is more prevalent among academia than those practicing public health in non-profit organizations and government agencies.¹⁸

Among healthcare workers, less work experience, pressure from management, regret over patient-related decisions, workplace harassment, and sacrificing personal time for work are all risk factors for burnout.¹⁹ Factors such as high work demands, concerns about patient care, unfavorable work environments, poor work-life balance, poor mental and physical health, financial concerns, and low self-efficacy are associated with an increased likelihood of experiencing burnout.²⁰ Additionally, 2 landmark studies on physician burnout showed that female physicians reported burnout symptoms at a higher rate than their male counterparts.^21,22 Another study of general practitioners revealed that female physicians were more likely to experience lower self-efficacy—a risk factor for burnout.²³ Further, a 2024 systematic literature review found that women in healthcare experience significantly more stress and burnout compared to their male counterparts. Gender inequality in the workplace, poor work-life integration, and a lack of autonomy contributed to these gender differences in burnout among healthcare workers.²⁴

The Maternal and Child Health (MCH) workforce includes professionals in public health, healthcare, community-based, governmental, and other organizations. These roles include but are not limited to employees in health departments and community-based organizations, doulas, midwives, birthing assistants, lactation consultants, home healthcare workers, neonatologists, obstetricians, and gynecologists. Many of these professions are subject to long hours, sensitive topics, and heavy workloads, making them susceptible to burnout.^13-15,18 Additionally, women make up the majority of the healthcare and public health workforce, especially in MCH, and are more likely than men to experience burnout, primarily due to workplace discrimination.^20,23-26

Additionally, the Dobbs decision uniquely affects the MCH workforce. A national survey demonstrated that the Dobbs decision has resulted in obstetrician-gynecologists (OBGYNs) feeling increased stress surrounding legal risks and reduced decision-making autonomy—a significant risk factor for burnout. Furthermore, OBGYNs in states that have restricted abortion also report that their quality of care and ability to manage emergency events has decreased due to the Dobbs decision.²⁷ OBGYNs have also reported mental distress, depression, anxiety, and intentions to leave their jobs as a result of the Dobbs decision.²⁸ Due to liability concerns and lack of autonomy, approximately 60% of OBGYNs report not wanting to take a job opportunity in a state with heavily restricted access to abortion.²⁹

These pressures resulting from the Dobbs decision are compounded by 2 other concurrent crises: rising maternal mortality/morbidity rates and COVID-19. Nurses report feeling overwhelmed and unable to provide adequate care for women experiencing severe maternal morbidity during childbirth, thus contributing to burnout.³⁰ In addition, the MCH workforce faced extreme stress and burnout during the pandemic, as evidenced by 73% of maternal and neonatal healthcare workers reporting that burnout increased among their co-workers.^31,32 Furthermore, several healthcare specialties in the category of MCH, like pediatrics, obstetrics and gynecology, and family medicine, reported the highest levels of burnout during the pandemic.²² Prior to the pandemic, midwives demonstrated a high prevalence of burnout.³³ Burnout among doulas has not been examined much at all, and burnout among both midwives and doulas post-Dobbs is likewise understudied.

Given the high susceptibility to burnout among the MCH workforce and recent events—the Dobbs decision, the maternal mortality crisis in the United States, and COVID-19—it is paramount to study burnout among this population. Researchers have studied burnout among healthcare providers for several decades, and the burnout of public health employees has gained national attention since the COVID-19 pandemic.^1,18 However, studies have yet to examine burnout and subjective well-being specifically for the MCH workforce. To fill this gap, this study examined the relationship between burnout and subjective well-being in the MCH workforce. Examining this relationship is a decisive step toward building a resilient workforce and improving MCH indicators across the United States.

Methods

This study aims to conduct a quantitative, web-based, cross-sectional, anonymous survey to examine sociodemographic characteristics and factors associated with burnout and subjective well-being among the MCH workforce. The final sample consisted of 313 professionals working in the MCH field. Individuals aged 18 years or older, residing in the United States, and working in the MCH field (ie, as an employee at a health department, doula, midwife, birthing assistant, employee at a community-based organization, lactation consultant, home healthcare worker, neonatologist, and OBGYN) were eligible to participate in the survey. Data were collected directly from adults using a web-based, anonymous survey. Participants provided written informed consent before beginning the survey. Individuals who do not work in the MCH field, individuals under the age of 18, and individuals residing outside of the United States were excluded from the study. This study was approved by the George Washington University Institutional Review Board (NCR245549).

Data collection occurred from February 14 to March 5, 2024 on QualtricsXM. The sampling method used in this study is convenience sampling. Data collectors posted the survey link to their personal LinkedIn page and several online professional groups, including Reproductive Health Advocates and Women in Public Health on LinkedIn and the r/publichealth Reddit board. The survey link was also sent to professional organizations with audiences in the MCH field, such as the National Center for Education in Maternal and Child Health (NCEMCH), the American College of Obstetricians and Gynecologists (ACOG), the National Association of Certified Professional Midwives (NACPM), and others. Additionally, data collectors contacted the MCH Directors of all states and asked them to share the survey with their MCH staff at both state and local health departments. Data collectors sent the survey to 82 sources (ie, professional groups, health departments, organizations, and associations).

The survey captured sociodemographic information about age, residence, employment status, sex, gender, race, ethnicity, family composition, education, income, profession, career length, and career specialty with 15 questions. The survey also included validated instruments such as the Maslach Burnout Inventory-Human Services Survey (MBI-HSS)² and the short scale of the Oxford Happiness Questionnaire (OHQ) to measure burnout and subjective well-being, respectively.³⁴ These instruments have previously been used to assess burnout and subjective well-being in similar populations.

The MBI-HSS consists of 22 statements of job-related feelings and measures 3 dimensions of well-being: (1) emotional exhaustion, (2) personal accomplishment, and (3) depersonalization.² Participants were directed to read the 22 statements and select the number corresponding to how often they felt each statement. The answer options were formatted on a Likert scale, ranging from 0 (never) to 6 (every day). Three subscales were created to measure each dimension of burnout. The emotional exhaustion subscale indicates the sum of answers to 9 statements, the depersonalization subscale reflects the sum of answers to 5 statements, and the personal accomplishment subscale is the sum of answers to 8 statements. An MBI-HSS scale was created by summing the scores from the 3 subscales. The scores for personal accomplishment statements were reverse-coded when making the MBI-HSS scale so that high scores reflected low personal accomplishment, which contributes to burnout. Higher scores on the MBI-HSS scale, the emotional exhaustion subscale, and the depersonalization subscale indicate higher levels of burnout. In comparison, higher scores on the personal accomplishment subscale indicate lower levels of burnout.

The OHQ short scale measures subjective well-being and consists of 8 statements.³⁴ Participants were instructed to indicate how much they agreed or disagreed with each statement by selecting a number ranging from 1 (strongly disagree) to 6 (strongly agree). The OHQ score reflects an average of all statements, with a higher score indicating higher subjective well-being (happiness) and a lower score indicating lower subjective well-being.

Data cleaning and analysis took place in SPSS Version 28.0.0.0. Individuals who did not complete both the MBI-HSS and the OHQ short scale were removed from the study sample. Additionally, response patterns and time spent taking the survey were used to identify unusual behavior that might indicate bot activity. Correlations, t-tests, and one-way ANOVAs were run to determine if there were significant associations between burnout, subjective well-being, and sociodemographic factors. These results informed which variables were included in multivariable linear regression.

Results

The descriptive characteristics of the study sample are shown in Table 1. Nearly half of the study sample is between the ages of 25 and 34, and almost 90% of the sample is female. The sample is 84.7% White, 6.4% Black or African-American, and 8.9% identify as a race other than White or Black. The sample is predominantly non-Hispanic, employed full-time, and makes less than $100,000 annually. Nurses comprise the most significant portion of the sample at 36.8%, followed by health department employees, who account for one-quarter, and physicians, who constitute over 10%.

Table 1.

MCH Workforce in the United States, Study Sample Characteristics (N = 313).

	N (%)
Age (years)
18-<25	23 (7.3)
25-<35	135 (43.1)
35-<45	67 (21.4)
45-<55	56 (17.9)
≥55	32 (10.2)
Employment status
Employed full-time	287 (91.7)
Employed part-time	26 (8.3)
Sex
Female	273 (88.1)
Male	37 (11.9)
Gender
Woman	274 (87.5)
Man	36 (11.5)
Number of children
0	64 (20.4)
1	98 (31.3)
2	96 (30.7)
3+	55 (17.6)
Ages of children (years)
Under 6	91 (48.9)
7-10	26 (14)
11-18	23 (12.4)
19+	46 (24.7)
Household income
Less than $100K	127 (40.6)
$100,000-$150,000	105 (33.5)
$151,000-$200,000	62 (19.8)
Hispanic/Latino origin
Yes	71 (22.8)
No	240 (77.2)
Race
White	265 (84.7)
Black/African-American	20 (6.4)
Other	28 (8.9)
Occupation
Nurse	111 (36.8)
Physician	33 (10.9)
Doula/birthing assistant	12 (4)
Midwife	15 (5)
Health department employee	75 (24.8)
Non-profit/community-based organization employee	25 (8.3)
Other	31 (10.3)
Healthcare specialty
Family medicine, pediatrics, and general internal medicine	47 (32.4)
Gynecology, labor and delivery, obstetrics, and neonatology	90 (62.1)
Other	8 (5.5)
	N (%)
Length of time in field (years)
<5	93 (29.7)
5-<10	83 (26.5)
10-<15	53 (16.9)
15-<20	36 (11.5)
≥20	48 (15.3)
Educational attainment
Less than Bachelor’s degree	78 (25.5)
Bachelor’s degree	122 (39.9)
Master’s degree or higher	106 (34.6)
Scale	Mean	SD	Maximum	Minimum	Cronbach’s α
OHQ short scale	4.3654	0.87426	1	6	.768
MBI-HSS scale	40.6358	20.30003	0	132	.888
Emotional exhaustion subscale	21.7827	12.26408	0	54	.918
Personal accomplishment subscale	35.7549	9.46282	0	48	.879
Depersonalization subscale	6.607	6.01085	0	30	.748

As shown in Table 1, each continuous scale showed sufficient reliability using Cronbach’s alpha. The mean score (out of 6) on the OHQ scale was 4.365 (SD = 0.874). The mean score (out of 132) on the MBI-HSS scale was 40.636 (SD = 20.300). The mean score (out of 54) on the emotional exhaustion subscale was 21.783 (SD = 12.264). The mean score (out of 30) on the depersonalization subscale was 6.607 (SD = 6.011). Finally, the mean score (out of 48) on the personal accomplishment subscale was 35.755 (SD = 9.463).

Table 2 presents multivariable linear regression models of burnout, emotional exhaustion, personal accomplishment, depersonalization, and subjective well-being. These 5 continuous variables are set as the dependent variable for each model. Linear regression controlled for subjective well-being, emotional exhaustion, personal accomplishment, depersonalization, age, employment status, sex, number of children, race, income, occupation, and career length.

Table 2.

Multivariable Linear Regression Models of Burnout, Emotional Exhaustion, Personal Accomplishment, Depersonalization, and Subjective Well-being.

Variable	MBI-HSS	Emotional exhaustion	Personal accomplishment	Depersonalization	OHQ
Variable	β (CI)	β (CI)	β (CI)	β (CI)	β (CI)
OHQ	−15.355 (−17.641 to −13.070)***	−6.048 (−7.467 to −4.628)***	5.602 (4.085-7.119)***	.158 (−0.697 to 1.013)	—
Emotional Exhaustion	—	—	.113 (−0.014 to 0.241)	.297 (0.242 to 0.352)***	−.036 (−0.044 to −0.027)***
Personal Accomplishment	—	.105 (−0.013 to 0.223)	—	−0.031 (−0.094 to 0.032)	.003 (0.022-0.039)***
Depersonalization	—	1.043 (0.851 to 1.235)***	−0.119 (−0.359 to 0.121)	—	.003 (−0.014 to 0.021)
Age (years)
18-<25 (Ref)	1.00	1.00	1.00	1.00	1.00
25-<35	−1.544 (−9.327 to 6.239)	−.725 (−4.865 to 3.416)	3.286 (−1.001 to 7.573)	.919 (−1.288 to 3.126)	−.039 (−0.358 to 0.279)
35-<45	−1.734 (−10.811 to 7.343)	1.840 (−3.005 to 6.684)	4.989 (−0.015 to 9.994)	−.162 (−2.749 to 2.425)	−.115 (−0.487 to 0.258)
45-<55	−5.603 (−15.891 to 4.685)	3.265 (−2.233 to 8.764)	5.478 (−0.215 to 11.170)	−2.175 (−5.104 to 0.755)	−0.030 (−0.454 to 0.394)
≥55	−5.603 (−23.956 to 0.852)	2.654 (−4.008 to 9.316)	7.345 (0.470 to 14.220)*	−3.245 (−6.781 to 0.291)	−0.191 (−0.703 to 0.322)
Sex
Female (Ref)	1.00	1.00	1.00	1.00	1.00
Male	5.949 (−0.231 to 12.128)	−1.864 (−5.194 to 1.466)	−4.044 (−7.478 to −0.609)*	1.889 (0.124 to 3.655)*	−.108 (−0.364 to 0.148)
Number of children
0 (Ref)	1.00	1.00	1.00	1.00	1.00
1	3.776 (−1.521 to 9.072)	−3.112 (−5.972 to −0.253)*	−.760 (−3.759 to 2.238)	2.909 (1.413 to 4.406)***	.027 (−0.195 to 0.249)
2	.279 (−5.357 to 5.915)	−4.624 (−7.632 to −1.617)**	−0.611 (−3.793 to 2.570)	2.681 (1.082 to 4.281)**	−.150 (−0.384 to 0.085)
3+	4.418 (−2.169 to 11.005)	.038 (−3.465 to 3.542)	−0.890 (−4.531 to 2.750)	1.197 (−0.667 to 3.060)	−.069 (−0.338 to 0.200)
Race
White (Ref)	1.00	1.00	1.00	1.00	1.00
Black	1.560 (−6.356 to 9.475)	5.102 (0.869 to 9.335)*	−2.992 (−7.426 to 1.442)	−3.450 (−5.693 to −1.207)**	−.091 (−0.420 to 0.238)
Other	−1.685 (−8.299 to 4.929)	4.141 (0.598 to 7.684)*	3.929 (0.239 to 7.619)*	−1.851 (−3.747 to 0.045)	−.254 (−0.528 to 0.019)
Occupation
Nurse (Ref)	1.00	1.00	1.00	1.00	1.00
Physician	1.148 (−5.629 to 7.925)	−.378 (−3.968 to 3.212)	−1.391 (−5.120 to 2.338)	.189 (−1.727 to 2.104)	.237 (−0.038 to 0.511)
Doula/birthing assistant	−0.471 (−10.639 to 9.696)	3.331 (−2.058 to 8.720)	.492 (−5.126 to 6.111)	−2.007 (−4.880 to 0.866)	.090 (−0.325 to 0.506)
Midwife	−5.889 (−14.723 to 2.945)	−1.575 (−6.267 to 3.117 )	.230 (−4.653 to 5.112)	−.974 (−3.476 to 1.529)	−.209 (−0.570 to 0.151)
Health department employee	−.432 (−5.621 to 4.757)	4.241 (1.464 to 7.019)**	−.224 (−3.162 to 2.715)	−2.759 (−4.228 to −1.290)***	−.207 (−0.423 to 0.009)
Non-profit employee	−8.117 (−15.301 to −0.932)*	.689 (−3.185 to 4.562)	1.089 (−2.937 to 5.115)	−2.821 (−4.858 to −0.783)**	−0.455 (−0.748 to −0.162)**
Other	−4.435 (−10.934 to 2.064)	.720 (−2.743 to 4.182)	1.252 (−2.346 to 4.850)	−1.546 (−3.384 to 0.292)	2.174 (−0.440 to 0.091)
Length of time in field (years)
<5 (Ref)	1.00	1.00	1.00	1.00	1.00
5-<10	2.688 (−2.545 to 7.921)	1.069 (−1.713 to 3.850)	.827 (−2.067 to 3.720)	.520 (−0.965 to 2.004)	.065 (−0.149 to 0.279)
10-<15	2.806 (−3.856 to 9.469)	2.425 (−1.101 to 5.951 )	−0.246 (−3.925 to 3.432)	−.583 (−2.469 to 1.304)	.046 (−0.226 to 0.318)
15-<20	−1.697 (−9.618 to 6.223)	2.738 (−1.486 to 6.961)	−1.342 (−5.744 to 3.060)	−2.626 (−4.864 to −0.389)*	−.076 (−0.402 to 0.249)
≥20	3.812 (−5.796 to 13.419)	3.052 (−2.038 to 8.141)	−2.173 (−7.472 to 3.127)	−1.209 (−3.927 to 1.510)	.364 (−0.026 to 754)

These models also controlled for employment status and income.

p < .05. **p < .01. ***p < .001.

Multivariate analysis yielded several statistically significant results. After controlling for sociodemographic and other factors, subjective well-being maintains a significant relationship with burnout, emotional exhaustion, and personal accomplishment. Specifically, a 1-point increase on the OHQ scale is associated with a 15.4-point decrease on the MBI-HSS scale, a 6.0-point decrease on the emotional exhaustion subscale, and a 5.6-point increase on the personal accomplishment subscale (p < .001 for all tests). These results suggest that higher subjective well-being is associated with lower burnout, lower emotional exhaustion, and higher personal accomplishment.

Furthermore, there were significant associations between subjective well-being and the emotional exhaustion and depersonalization subscales of burnout. A 1-point increase on the emotional exhaustion subscale is associated with a 0.04-point decrease on the OHQ scale and a .3-point increase on the depersonalization subscale (P < .001 for both tests). These results indicate that emotional exhaustion is significantly associated with lower subjective well-being and higher depersonalization. Additionally, a 1-point increase on the personal accomplishment subscale is expected to result in 0.003 points more on the OHQ scale (p < .001), implying that higher personal accomplishment is associated with higher subjective well-being.

Results also varied according to age, sex, number of children, race, occupation, and career length. Notably, males scored 4.0 lower than females on the personal accomplishment subscale (p = .021) and 1.9 higher on the depersonalization subscale (p = .036). Additionally, compared to White respondents, Black respondents scored 5.1 higher on the emotional exhaustion subscale (p = .018), and respondents of another race (other than White or Black) scored 4.1 higher (p = .022). Conversely, Black respondents scored 3.5 less on the depersonalization scale than White respondents (p = .003), and respondents of another race scored 3.9 higher on the personal accomplishment scale (p = .037). Finally, compared to nurses, non-profit employees scored 8.1 less on the MBI-HSS scale (p = .027), 2.8 lower on the depersonalization subscale (p = .007), and 0.5 lower on the OHQ scale (p = .002). Health department employees likewise scored 2.8 lower on the depersonalization subscale than nurses (p < .001) but 4.2 higher on the emotional exhaustion subscale (p = .003).

Discussion

The findings of this study indicate that burnout is significant within the MCH workforce. The mean scores on the MBI-HSS suggest that MCH professionals experience moderate levels of burnout, with particularly notable levels of emotional exhaustion. This finding is consistent with previous research highlighting the demanding nature of MCH professions and the emotional labor involved in providing care and support to mothers and children.^22,31,32,35

In contrast, the average score on the short scale of the OHQ suggests that MCH professionals report relatively high levels of subjective well-being. This finding may reflect the intrinsic rewards of working in roles that support MCH. However, it is essential to recognize that subjective well-being does not necessarily negate the presence of burnout, and interventions aimed at addressing burnout should be prioritized to ensure the long-term well-being of MCH professionals.

The multivariate analysis revealed several significant associations between sociodemographic factors and burnout among MCH professionals. Consistent with previous research,^3-5 subjective well-being was inversely related to burnout, emotional exhaustion, and depersonalization, highlighting the importance of promoting happiness as a protective factor against burnout.

Additionally, certain demographic factors, such as sex and race, were found to influence burnout levels among MCH professionals. For example, male MCH professionals reported lower levels of personal accomplishment and higher levels of depersonalization than their female counterparts. Similarly, Black MCH professionals reported higher levels of emotional exhaustion but lower levels of depersonalization compared to White professionals. These findings underscore the importance of considering intersectional factors in understanding and addressing burnout within the MCH workforce.

This study has several strengths, including its use of validated measures such as the MBI-HSS and the OHQ short scale. Each continuous scale and subscale also demonstrated high reliability indicated by Cronbach’s alpha. The sample of 313 MCH professionals encompasses a diverse range of backgrounds and roles within the field, providing a rich and comprehensive perspective on burnout and subjective well-being among this vital workforce. Additionally, the utilization of multivariable analysis allows for a comprehensive exploration of associations between burnout, subjective well-being, and sociodemographic factors while controlling for covariates to enhance the validity of the results.

It is essential to acknowledge the limitations of this study, including its cross-sectional design, reliance on self-reported data, and limited generalizability. The cross-sectional design restricts the study’s ability to establish causal relationships or capture changes in burnout and subjective well-being over time among MCH professionals. Furthermore, the reliance on self-reported data may introduce biases such as social desirability bias, recall bias, and response bias, potentially influencing the validity and reliability of the findings regarding burnout and subjective well-being. Future research should employ longitudinal designs to examine the trajectory of burnout among MCH professionals over time and explore the effectiveness of interventions in mitigating burnout and promoting well-being. Additionally, this study focused on burnout and subjective well-being among MCH professionals in the United States, limiting the generalizability of the findings to other contexts. Future research should explore cultural and contextual factors influencing burnout among MCH professionals globally to develop culturally sensitive interventions. Furthermore, subsequent studies should consider exploring the relationship between burnout and other dimensions of mental health, including measuring depression and anxiety.

Burnout among the MCH workforce has profound implications, especially considering that there is already a shortage of healthcare professionals in rural communities, with over 2.2 million women in the United States living in maternity care “deserts” and an additional 4.7 million women living in areas with limited access to OBGYNs.³⁶ These statistics are particularly concerning, especially considering that most OBGYNs are only a few years away from retirement.^37,38

In addition, 83 million people live in areas with limited access to primary care physicians,³⁹ and by 2036, the United States will face a shortage of 20,200 and 40,400 primary care physicians.⁴⁰ Moreover, nearly half of all state and local public health employees reported considering leaving their positions within the next 5 years,¹⁷ and over a quarter of healthcare workers wanted to leave their positions due to the pandemic.⁴¹ Many of these public health and healthcare workers cited work overload and burnout as reasons to leave their jobs.^17,41,42 If burnout continues to push critical MCH professionals out of the field, access to care and services will become even more limited than it already is.

These findings can yield several recommendations. At the policy level, organizational reforms are crucial for establishing supportive workplace environments. Policies promoting work-life balance, flexible scheduling, and protections against harassment and discrimination are paramount. Additionally, resource allocation should prioritize mental health services and resilience training. Legislative measures can also be pivotal, mandating staffing levels, limiting work hours, and enforcing protections against workplace mistreatment.

Programs targeting subjective well-being and burnout must adopt a holistic approach, integrating individual and organizational interventions. Such programs should include strategies for promoting healthy lifestyle behaviors, managing stress, and building social connections and be tailored to address the specific needs of different subgroups within the MCH workforce. For example, interventions aimed at reducing burnout among male professionals may need to focus on strategies for enhancing personal accomplishment and reducing depersonalization. Similarly, interventions for Black professionals may need to address the systemic factors contributing to higher levels of emotional exhaustion while capitalizing on lower levels of depersonalization. Early intervention necessitates proactive screening for burnout symptoms, confidential counseling services, and peer support networks.

Conclusion

The study’s findings indicate that higher subjective well-being is significantly associated with lower burnout, emotional exhaustion, and higher personal accomplishment, while variations in burnout and well-being are also influenced by sociodemographic factors such as age, sex, race, and occupation. This study provides valuable insights into the prevalence of burnout, associated factors, and the relationship between burnout and subjective well-being among MCH professionals. By addressing these challenges and tailoring interventions to the unique needs of MCH professionals, we can foster a more supportive and resilient workforce dedicated to promoting the health and well-being of mothers and children.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319241263443 – Supplemental material for Subjective Well-Being and Burnout Among the Maternal and Child Health Workforce

Supplemental material, sj-docx-1-jpc-10.1177_21501319241263443 for Subjective Well-Being and Burnout Among the Maternal and Child Health Workforce by Carolyn Brown-Kaiser and Amita Vyas in Journal of Primary Care & Community Health

Footnotes

Acknowledgements

The authors are incredibly grateful to the busy Maternal and Child Health professionals who took the time to participate in this study. We also thank Shikha Chandarana for her assistance.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the George Washington University Center of Excellence in Maternal and Child Health under Grant No T76MC35370 from the Health Resources & Services Administration (HRSA) Maternal and Child Health Bureau. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Health Resources & Services Administration (HRSA) Maternal and Child Health Bureau.

ORCID iD

Carolyn Brown-Kaiser

Supplemental Material

Supplemental material for this article is available online.

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