Contextual Factors Associated With Readiness to Implement Universal Prevention Programs in Rural Schools

Abstract

Background

Despite the importance of organizational readiness for implementing evidence-based practices in schools, few studies have empirically examined contextual and attitudinal factors that may shape how implementation team members perceive their schools’ preparedness to implement new practices. We examined associations between theorized factors and perceptions of organizational readiness among school teams preparing to launch a universal prevention initiative.

Method

Data came from implementation team members (n = 166) from 40 Idaho schools participating in the baseline wave of a hybrid Type 3 effectiveness-implementation trial of supports to improve scale-up of universal prevention. Multilevel regression models tested individual and school-level predictors of perceived team and staff readiness, including indicators of task demands, resource availability, and situational factors.

Results

Analyses indicated positive associations of system support and protective factors with both team and staff readiness, as well as belief in the program and school resources as predictors of team members’ readiness, and transformational leadership as a predictor of perceived staff readiness.

Conclusions

Findings highlight the importance of fostering positive attitudes toward evidence-based practices to enhance team members’ perceptions of readiness to implement and potentially improve downstream success. Administrators seeking to strengthen the implementation of evidence-based programming may also benefit from targeted investments connecting staff members to available resources.

Plain Language Summary

What Helps School Staff Feel Prepared to Implement Universal Prevention Programs?

Organizational readiness is key to preparing schools to implement prevention programs for students’ behavioral health. Readiness refers to the motivation and capacity of an organization and its staff to launch a new program or practice. In schools, implementation teams—consisting of small groups of administrators, counselors, and teachers—are frequently the individuals responsible for adopting and structuring these programs, while counselors and teachers are often responsible for delivering the program in classrooms. Despite its importance, we know little about what makes school staff feel prepared to start these new programs. Some research suggests that personal factors, such as a belief in the program, are key elements, as are organizational factors, such as the availability of school and system resources. To determine which factors are most impactful, we surveyed 166 implementation team members across 40 rural schools that were in the early stages of a new schoolwide prevention program. The survey asked about several personal and structural factors theorized to be related to their feelings of readiness, as well as how prepared they thought school staff were to use the program. We found that system support and school protective factors were most closely related to greater readiness for both teams and staff. Individual factors, including one's belief in the program, were also important factors to team members, as were resources available to the school. School staff were also thought to be more prepared when they believed their principals were committed to change. Support and resources from the school and system seemed to have a greater impact than risk factors or personal resources in helping team members and staff feel prepared. These findings will help schools and researchers build better and more efficient ways to improve motivation and staff's ability to implement these programs, potentially even improving the programs’ effectiveness.

Keywords

implementation science organizational culture protective factors schools evidence-based practice readiness to implement

Introduction

Universal and multi-tiered prevention approaches in schools, such as Positive Behavioral Interventions and Supports (Sugai & Horner, 2002) and multi-tiered systems of supports (Lane et al., 2013), are increasingly common evidence-based frameworks to improve their disciplinary environments and practices. These tiered systems offer a whole-of-school approach toward student support, and can improve both academic and behavioral outcomes among students (Bradshaw et al., 2012), as well as organizational health and teacher well-being (Bradshaw et al., 2008; Ross et al., 2012). As with other evidence-based practices, the scope and success of these frameworks and their related outcomes are strongly tied to school staff's capacity to implement these practices with fidelity. An important contributor to implementation with high levels of fidelity is an organization's readiness to implement; however, the empirical research on readiness in school-based implementation research has been relatively limited (Cook et al., 2023). Prior research examining organizational readiness has largely focused on measurement, which is a necessary precondition for empirical work; however, the studies developing measurement tools, as well as those exploring the impact of readiness on implementation fidelity, have been conducted in health services settings (Weiner, Amick, et al., 2008). Schools have a unique personnel infrastructure for implementation practices, including a more involved and centralized administrative hierarchy, and as such, implementation in these settings may require different strategies.

Evidence-based programming in schools, particularly for multi-tiered and universal behavioral health practices, frequently requires diverse implementation leadership teams, including teachers, counselors, and administrators, and sometimes representation from students, parents, and community members. Multiple factors, such as the capability of these teams to prepare their schools for change, are critical to fostering faithful implementation (Meyers et al., 2012). Like in healthcare settings, implementation teams in schools may be bolstered by the availability of organizational resources or constrained by the imposition of environmental deficiencies and barriers to implementation (Splett et al., 2022). Although readiness is essential (Cook et al., 2023; Hustus & Owens, 2018; Weiner, 2009), little is known about the specific contextual factors that influence school leadership teams’ readiness to implement new evidence-based practices, nor is it clear which factors promote or inhibit readiness among the school staff responsible for direct classroom implementation. Further, research on the effect of individual perceptions of these factors is limited. Identifying the most salient traits will assist interventionists, school administrators, districts, and states in providing more efficient, effective, and targeted supports to improve the fidelity of implementation as well as long-term maintenance.

Organizational Readiness for Implementation

Organizational readiness is a multi-faceted concept used to frame overlapping capacities for the successful activation of change in an organization. It can describe either individuals or organizations as either agents or targets of change (Mueller et al., 2012). Frequently, researchers use organizational readiness as a broad conceptualization of both individual and organizational structures that predict successful early implementation of an intervention. For example, the R = MC2 heuristic (Readiness = staff Motivation, organizational Capacities, and innovation-specific Capacities; Scaccia et al., 2015) identifies the primary needs that must be addressed to maximize success when implementing innovations. Because organizational readiness is oriented toward successful change, such frameworks align with implementation frameworks more generally. For example, the widely used Consolidated Framework for Implementation Research (Damschroder et al., 2009, 2022) defines the individuals involved, the inner and outer settings, and an innovation's characteristics and processes as the necessary determinants relevant to an intervention's overall success.

In practice, the component parts of organizational readiness outlined in such frameworks interact with one another (Mueller et al., 2012). Prior research on organizational readiness has commonly focused on staff's preparedness and motivation to implement change (Mueller et al., 2012; Weiner, Amick, et al., 2008), and how that motivation is impacted by the contextual factors of the intervention itself (Shea et al., 2014; Weiner, 2009). Structural elements—the organizational capacities and intervention characteristics—can also function as determinants of individuals’ psychological motivation to prepare their organizations for change. Weiner (2009) further delineates these potential determinants into task demands, resource availability, and situational factors to theorize mechanisms that influence an individual's motivation to implement change. For example, organizational capacity, in the form of material or personnel resources, can influence staff's beliefs that they have sufficient practical tools to prepare their organization for change.

Organizational Readiness in Schools

Similar to research in healthcare and community-service organizations, organizational readiness in schools is associated with successful implementation outcomes, including staff adoption (Cook et al., 2023; Hustus & Owens, 2018), implementation fidelity (Bast et al., 2021), and student outcomes (Lynch et al., 2019). However, research identifying factors that influence school staff's readiness to implement change has been sparse. Additionally, in schools, staff readiness may include both the implementation team members, who are responsible for building the implementation framework and preparing the broader school environment for implementation, as well as the teachers and other school staff who are most directly responsible for classroom-based implementation and activities (Franklin et al., 2012). Applying Weiner's (2009) framework of task demands, resource availability, and situational factors to the specifics of school environments, we can identify characteristics expected to be associated with the readiness of both implementation team members and school staff.

Task demands are the actions required based on the needs of the innovation (Caci et al., 2025; Weiner, 2009). As these requirements are identified through the appraisal of the team members responsible for the task, they must have knowledge of the specific needs of the intervention to estimate whether the organization is prepared for its implementation. As this interpretation is relative, the team member must also estimate whether the intervention will be effective with the set of available resources and within the specific context of their environment (Weiner, 2009). Belief in the effectiveness of an intervention may also be a driver of implementation, as building positive beliefs about interventions has been demonstrated to improve uptake (Kallitsoglou, 2020).

In addition to understanding the requirements of the intervention, not only must sufficient resources be available to successfully implement it, but an individual must also believe they have access to those resources. Resources can include personal resources, such as equipment and documentation, or school-level resources, such as infrastructure and financial resources, which may be necessary to effectively implement a change. Likewise, having sufficient time resources to engage in implementation practices, such as training and planning, is a major contributor to readiness among school staff, such as mental health professionals (Splett et al., 2022). Personnel resources, such as effective in-school leadership support for change, may also influence motivation by inspiring staff and providing individual support (Bass & Avolio, 1994). Due to the centralized nature of the school administrative hierarchy, the investment of principals in school-wide evidence-based practices is a major factor in their ultimate success (Lyon & Bruns, 2019), and the transformational leadership of administrators is an effective way to improve staff attitudes toward these practices and their successful implementation (Farahnak et al., 2020).

Lastly, situational factors include traits of the broader environment that may promote or inhibit an individual's motivations and capacity to implement evidence-based practices. In schools, these may be defined by the level of support of behavioral health programming from system leadership (Brady et al., 2024) or a strong school disciplinary climate (Bradshaw et al., 2015). Other protective factors, such as positive work environments and existing successful integrations of student supports, are also likely to be more conducive to implementation success (Durlak & DuPre, 2008). Despite being external to the individual, such features may motivate staff through the personal belief that engaging in new practices will be consistently supported within a structured environment. Although school risk factors, such as safety concerns and greater levels of student disciplinary issues, have rarely been examined in relation to organizational readiness, such factors may reduce perceived readiness due to the actual or perceived diversion of resources elsewhere.

Although there are numerous potential factors that are likely to influence a school's organizational readiness, it is not clear which among these factors are the most salient to the motivations and capacities of implementation team members preparing their schools to implement evidence-based practices. Identifying traits that are most strongly associated with the readiness to implement among both implementation teams and among all school staff can be useful for school officials to determine how resources should best be used.

Current Study

This study examines predictors of organizational readiness among implementation team members at a sample of 40 schools in rural Idaho that were preparing to implement PBIS. We examine contextually specific predictors of school-based organizational readiness, including indicators of task demands, resource availability, and situational factors, to identify which among these are salient to school members responsible for preparing their schools to implement Tier 1 of PBIS, a universal (schoolwide) prevention initiative. Because universal school-based interventions rely on both implementation teams and other school staff, we examine both the team's perception of their own readiness to create organizational change, as well as their perception of the broader staff environment's readiness to accept and employ evidence-based programming. Further, we test this within a multilevel structure to differentiate and better specify individual- and school-level effects on these perceptions. Based on prior theory, we hypothesize significant, positive associations with all supports and negative associations with risk factors.

Methods

Procedure

Data come from the baseline wave of a cluster-randomized Type 3 effectiveness-implementation trial testing a package of supports designed to improve scale-up of Schoolwide Positive Behavioral Interventions and Supports (SWPBIS) in schools (Turner et al., 2022). In the 2018–2019 school year, K-12 schools throughout Idaho state which met the inclusion criteria (being in a rural or remote area, having more than 100 students, and having no prior experience implementing SWPBIS) were invited to participate. Of these, 186 schools declined to participate, and 40 agreed to participate in the trial, with 20 being randomized to receive training only and the other 20 receiving the intervention package of supports, including additional training and technical assistance. More details are available in Calvert et al. (2025). In Spring of 2019, school administrators and related personnel from all 40 schools received a brief orientation to SWPBIS, as well as assistance with the development of a school implementation team consisting of 5–8 members, including the principal, as well as a counselor if the school had one.

Sample

Implementation team members from all 40 schools were invited to complete an online survey. For the present study, respondents (n = 166) are those who completed the survey. Of the original sample of 166, nine respondents were not included in analyses due to missing data for all or nearly all study variables. The final analytic sample includes 157 implementation team members from across the 40 schools, with each school having between one and six respondents (M = 4.0; SD = 1.2). Among team members, 22.9% were school administrators, 60.5% were teachers, and 16.6% were counselors or other school staff. Of the 40 schools in the study, 23 (57.5%) were elementary or elementary/middle schools, eight (20%) were middle/high schools, four (10%) were high school only, and five (12.5%) were inclusive of all grades from pre-kindergarten or kindergarten through 12^th grade. Across schools, the percentage of students receiving free or reduced-price lunch ranged from 17.1% to 92.6%, with a mean of 48.5% (SD = 17.6%). The schools ranged in size from 94 to 780 students (M = 349, SD = 176) and had between 6 and 36 teachers (M = 19, SD = 7). One school fell below the initial 100-student inclusion threshold at the time of data collection due to disenrollment following sample selection.

In addition to data from implementation team members, survey data from school staff (n = 681) were aggregated at the school level to provide independent estimates of each school's greater staff environment. In Spring of 2019, shortly after the implementation team members were surveyed, school staff at each of the 40 schools were invited to take part in a brief survey regarding school climate. All members of the school staff, including teachers, administrators, and support staff, were able to complete the survey. Although some team members completed both surveys, aggregate data for this study used only data from school staff who were not also members of the SWPBIS implementation teams (n = 574). The number of staff respondents aggregated per school ranged from 2 to 34, with a mean of 14.4 (SD = 7.5) per school. Among staff, 85.2% self-identified as female and 13.0% identified as male, with the remaining proportion preferring not to answer. Reflecting the demographics of the region, 90.6% of staff identified as non-Hispanic White, 2.3% as Hispanic or Latino, and 2.2% as another ethnicity (Native American/American Indian, Asian/Pacific Islander, or Multi-racial), with 4.9% preferring not to respond. All study participants provided informed consent prior to each survey. The Institutional Review Board at Boise State University (protocol number 101-SB17-207) approved the original study protocol.

Measures

Measures for this study include two outcomes assessing team and staff readiness, 13 scales assessing elements of the school environment expected to relate to each type of readiness, and two school-level covariates. All scales were adapted from previously validated measures, with minor wording modifications, and in some cases, additional items to reflect the specific context of PBIS implementation.

Outcome variables included two scales derived from the Organizational Readiness for Implementing Change (Shea et al., 2014) scale (see Appendix for a list of all scales, including sample items and sources). Originally designed for use in healthcare settings, the wording of items was slightly adjusted to focus on school settings and SWPBIS and to reflect the two distinct referent groups (team vs. school staff). Responses to all items were coded 0 (“Strongly disagree”) to 5 (“Strongly agree”) and were averaged to create the scales. Organizational Readiness included six items (α = .94) from both the Change Commitment and Change Efficacy subscales and reflects respondents’ perception of confidence and motivation among implementation team members (e.g., “I feel confident that our team can get staff at this school invested in implementing SWPBIS”). Perceived Staff Readiness included three items (α = .92) from the Change Commitment subscale and reflects the respondents’ perception of the commitment toward implementation within the broader school staff environment (e.g., “Staff, including teachers, at this school will do what it takes to implement SWPBIS”).

Because the original scale items were slightly altered and organized differently for the present study, we examined their psychometric performance using confirmatory factor analysis. A two-factor model based on the theorized structure had adequate but suboptimal fit (CFI = .94, TLI = .92, SRMR = .04, RMSEA = .10). Because items in each of the two scales shared common stems (e.g., referring either to the team or to the school staff), we specified residual covariances among similar items within each scale to account for shared method variance (Podsakoff et al., 2003). This model demonstrated strong fit (CFI = .98, TLI = .96, SRMR = .03, RMSEA = .07), providing additional support for the structural validity of the adapted scales.

Two scales measured aspects of respondents’ task demands. Knowledge of evidence-based practices is a 6-item scale (α = .84) that assessed respondents’ knowledge of behavioral interventions and data-oriented decision making. The scale included four items from the practical and conceptual knowledge factors of the Response-To-Intervention Readiness and Implementation Survey (Fan et al., 2018), as well as two items generated for this study to assess respondents’ perceived knowledge of SWPBIS implementation and principles specifically. Belief in SWPBIS is a 4-item scale (α = .96) that assessed the respondent's views on the potential effectiveness of SWPBIS. The scale included one item from Fan et al.'s belief factor, reworded to focus on SWPBIS (“I believe SWPBIS can support successful learning outcomes for our students”), and three items generated for this survey regarding respondents’ belief in the specific effectiveness of SWPBIS on student behavior, school safety, and school climate. Responses ranged from 0 (“Strongly disagree”) to 5 (“Strongly agree”).

Four scales measured the respondents’ perceptions of resource availability. Time availability is a 3-item scale (α = .83) sourced from Fan et al.'s time factor and reworded slightly for this study's focus on SWPBIS. Scale items assessed the respondents’ personal availability of time to implement and engage with SWPBIS (e.g., “I have sufficient time to attend PBIS leadership team meetings.”) Responses ranged from 0 (“Strongly disagree”) to 5 (“Strongly agree”). Personal implementation resources is a two-item scale (r = .77) sourced from Fan et al.'s resource factor that assessed the respondents’ access to resources for implementation and progress monitoring of student behavior.

To provide an independent perspective of school resources, two scales were aggregated across staff for each school. Transformational leadership assessed staff's (n = 532) feelings about whether school administrators encouraged and communicated with staff. The scale was derived from the Global Transformational Leadership scale (Carless et al., 2000) and included eight items (α = .96), with response options ranging from 0 (“To a very small extent”) to 4 (“To a very large extent”). School resources is a seven-item scale (α = .75) sourced from the ED School Climate Surveys (National Center for Education Statistics, 2016) that assessed the availability of programs and services to the students (e.g., “This school has programs that address substance use among students”). Response options ranged from 0 (“Strongly agree”) to 3 (“Strongly agree”).

Lastly, four scales assessed situational factors and the broader school environment. System support is a 5-item scale (α = .82) that assessed respondents’ beliefs about the level of cross-level support for PBIS implementation in their schools. Two scale items were derived from Fan et al.'s system support factor and assessed perceived local stakeholder support, and three items were generated for this survey to assess perceptions of district, leadership, and staff support for positive behavior supports. School protective factors is an 11-item scale (α = .80) from the School Safety Survey (Sprague et al., 2013) and assessed a range of protective influences and supports that may have been available at the school, such as professional development, positive school climate, acceptance of diversity, and parent involvement. School risk factors, likewise, is a 13-item scale (α = .79) that included more detrimental influences, such as vandalism, high mobility, fights, and truancy. For each set of risk and protective factors, respondents identified the degree to which each was prevalent at their school, ranging from 0 (“Not at all”) to 3 (“Very much”). Order/discipline is a six-item scale (α = .85) aggregated from staff (n = 532) to the school level to assess average perceptions of the school's disciplinary environment. The scale combines two items from the Maryland Safe and Supportive Schools Climate survey (Bradshaw et al., 2014) with four items created for this survey, and broadly assesses the school's atmosphere and teachers’ abilities to communicate with students and handle disruptions. Response options ranged from 0 (“Strongly disagree”) to 3 (“Strongly agree”).

School-level covariates included school grade level (Elementary/Middle, Middle/High, High school, and all grades K-12) and percentage of students who were eligible for free or reduced-price meals as a proxy for socioeconomic disadvantage (less than 40%, 40–60%, greater than 60%).

Analyses

As a first step, we examined the descriptive statistics and zero-order correlations among all study variables to identify their unadjusted associations. We next assessed the associations between the theorized independent variables and the outcomes of organizational readiness and perceived staff readiness by conducting two parallel multilevel linear regression models. Individual-level only predictors included knowledge of SWPBIS, belief in SWPBIS, time availability, and personal implementation resources, and were grand-mean centered. School-level predictors included grand-mean-centered aggregated staff scores at each school for perceived leadership, school resources, and school order/discipline, as well as controls for both school grade levels and the proportion of students at the school receiving free or reduced-price lunches. Additionally, three variables that captured perceptions of school situational factors (system support, school protective factors, and school risk factors) were expected to vary within and between schools and were therefore decomposed into individual- and school-level components, with group-mean centering at the individual-level and grand-mean centering at the school-level. Because the decomposed variables were assessed by team members and members were exclusive to each school, their individual-level coefficients can be interpreted as the deviation of the individual from their team average, and school-level coefficients as the team's deviation from the average among teams.

Both models used the sandwich estimator to adjust standard errors for potential bias due to clustering. Although missing data was low (<3%) across study variables, to reduce further the chance of potential bias due to non-response and to maintain statistical power, models accounted for missing data using multiple imputation (m = 100), accounting for the clustering of individuals within schools. Model results were then pooled following Rubin (1987). All statistical analyses were conducted in Mplus 8.11 (Muthén & Muthén, 2017).

Results

Table 1 shows the descriptive statistics and zero-order correlations among study variables. The correlation between the two dependent variables was moderate (r = .60, p < .001), and both were observed to be normally distributed via Q-Q plots and skewness and kurtosis values less than [.50]. Correlations between these two variables and the independent variables were low to moderate magnitude (.01 to .66). Significant correlations were all in the theorized directions, with positive predictors, such as knowledge and the availability of resources, being positively correlated with each outcome and one another, and associations between school risk factors being negative with both outcomes and positively oriented predictors. Generally, measures provided by the respondents had greater magnitude correlations and were more consistently significant than those aggregated from school staff, although aggregate transformational leadership and order/discipline were significant in their associations with both dependent variables.

Table 1.

Zero-Order Correlation Table and Descriptive Statistics.

	1	2	3	4	5	6	7	8	9	10	11	12
1. Organizational readiness
2. Staff readiness	.60***
3. Knowledge in SWPBIS	.14^†	.14^†
4. Belief in SWPBIS	.49***	.32***	.32***
5. Time availability	.46***	.40***	.23**	.33***
6. Implementation resources	.26**	.34***	.32***	.19*	.40***
7. System support	.60***	.66***	.14*	.38***	.51***	.41***
8. School protective factors	.30***	.41***	.14†	.09	.08	.30***	.30***
9. School risk factors	−.12	−.16*	.11	.08	−.02	−.03	−.10†	−.29**
10. Transformational leadership^a	.15*	.20**	.06	.10	.11	−.06	.14†	.06	.07
11. School resources^a	.14	.01	.06	.05	.04	−.03	.07	.04	.00	.60***
12. Order/discipline^a	.18*	.26**	−.01	.10	.06	.03	.17*	.19	−.21	.70***	.58***
M	4.08	3.25	3.06	4.18	3.1	2.88	3.58	2.01	1.03	2.84	1.76	1.93
SD	0.66	0.86	0.9	0.59	0.97	1.08	0.76	0.42	0.34	0.63	0.21	0.27
Range	0–5	0–5	0–5	0–5	0–5	0–5	0–5	0–3	0–3	0–4	0–3	0–3

Note. Significance tests based on cluster-adjusted standard errors.

SWPBIS = Schoolwide Positive Behavioral Interventions and Supports.

Staff mean scores per school (n = 574).

***p < .001 **p < .01 *p < .05 ^†p < .10.

Intercept-only regression models indicated small-to-moderate intracluster correlation coefficients (ICCs) of .06 for organizational readiness and .04 for perceived staff readiness, consistent with the small number of individuals per school and suggesting limited variance at the school level. However, to preserve the interpretability of the model estimates in distinguishing within- and between-school associations, we retained a multilevel modeling approach.

Table 2 shows the model estimates of a multilevel linear regression model predicting team members’ perceived organizational readiness. We observed significant positive associations with at least one indicator for each of task demands (belief in SWPBIS), resources (school resources), and situational factors (system support, school protective factors). Except for school resources, these effects occurred at the individual level, indicating that team members who held stronger beliefs in SWPBIS than the sample average reported greater organizational readiness, and those who held greater perceptions of system support and school protective factors than their team average similarly reported greater readiness. At the school level, team members reported greater readiness at schools identified by staff as having above-average resources. Among covariates, team members reported lower organizational readiness at schools with lower socioeconomic disadvantage (less than 40% of students eligible for free or reduced-price meals) compared to those at schools with moderate disadvantage (40–60% eligibility), and schools that serve all grade levels had lower reported readiness than elementary/middle schools. This model explained 43% of the variance of organizational readiness at the individual level and 98% at the school level, consistent with the small ICC. A similar model predicting perceived staff readiness (Table 3) similarly identified situational factors (system support, school protective factors) at the individual level, as well as resources (transformational leadership) at the school level, indicating that team members reported greater levels of staff readiness at schools identified by staff as having above-average transformational leadership. This model similarly explained 44% of the variance in the outcome variable at the individual level and 97% at the school level.

Table 2.

Multilevel Regression Model of Perceived Organizational Readiness on Predictors.

				95% CI
Variable	b	SE	z	Low	High	β
Individual-level
Knowledge in SWPBIS (D)	−0.03	0.08	−0.35	−0.18	0.13	−0.04
Belief in SWPBIS (D)	0.27	0.10	2.73**	0.08	0.46	0.27
Time availability (R)	0.15	0.08	1.95†	0.00	0.31	0.25
Personal implementation resources (R)	−0.05	0.06	−0.92	−0.16	0.06	−0.09
System support (S)	0.31	0.07	4.55***	0.17	0.44	0.39
School protective factors (S)	0.36	0.11	3.41**	0.15	0.57	0.23
School risk factors (S)	−0.18	0.12	−1.45	−0.42	0.06	−0.08
School-level
Intercept	2.96	18.08	0.16	−32.47	38.39	-
Transformational leadership^a (R)	−0.04	0.08	−0.55	−0.20	0.11	−0.19
School resources^a (R)	0.56	0.25	2.20*	0.06	1.05	0.75
Order/discipline^a (S)	−0.02	0.21	−0.10	−0.43	0.38	−0.04
System support (S)	0.43	4.19	0.10	−7.77	8.64	0.14
School protective factors (S)	−0.06	1.10	−0.06	−2.22	2.09	−0.07
School risk factors (S)	−0.18	1.29	−0.14	−2.70	2.34	−0.22
School grade level
Elementary/Middle (referent)
Middle/High	0.00	0.10	0.02	−0.20	0.21	0.01
High School only	0.07	0.16	0.44	−0.24	0.38	0.46
All Grades	−0.28	0.10	−2.75**	−0.47	−0.08	−1.83
Percent Free/Reduced Price Lunch
Less than 40%	−0.17	0.08	−2.20*	−0.33	−0.02	−1.15
40–60% (referent)
Greater than 60%	−0.17	0.10	−1.79^†	−0.36	0.02	−1.12
R-square (Within)	0.43
R-square (Between)	0.98

Note. Model was estimated using multiply imputed (m = 100) datasets. All variables are grand-mean centered except individual-level system support, school protective factors, and school risk factors, which are group-mean centered.

(D) = Task Demands; (R) = Resources; (S) = Situational Factors; SWPBIS = Schoolwide Positive Behavioral Interventions and Supports.

Staff mean scores per school (n = 574).

***p < .001 **p < .01 *p < .05 ^†p < .10.

Table 3.

Multilevel Regression Model of Perceived Staff Readiness on Predictors.

				95% CI
Variable	b	SE	z	Low	High	β
Individual-level
Knowledge in SWPBIS (D)	0.01	0.07	0.09	−0.14	0.15	0.01
Belief in SWPBIS (D)	0.06	0.12	0.51	−0.17	0.29	0.05
Time availability (R)	0.08	0.08	1.02	−0.07	0.22	0.10
Personal implementation resources (R)	0.02	0.06	0.32	−0.10	0.14	0.03
System support (S)	0.57	0.10	5.72***	0.37	0.76	0.55
School protective factors (S)	0.42	0.16	2.67**	0.11	0.74	0.21
School risk factors (S)	0.03	0.20	0.16	−0.35	0.41	0.01
School-level
Intercept	4.05	11.81	0.34	−19.11	27.20	-
Transformational leadership^a (R)	0.20	0.10	1.99*	0.00	0.39	0.56
School resources^a (R)	−0.58	0.39	−1.49	−1.35	0.19	−0.52
Order/discipline^a (S)	0.30	0.26	1.14	−0.21	0.80	0.35
System support (S)	0.05	5.23	0.01	−10.19	10.29	0.01
School protective factors (S)	−0.05	2.95	−0.02	−5.83	5.73	−0.04
School risk factors (S)	−0.75	2.25	−0.33	−5.16	3.67	−0.63
School grade level
Elementary/Middle (referent)
Middle/High	−0.01	0.22	−0.06	−0.45	0.42	−0.06
High School only	−0.06	0.18	−0.33	−0.40	0.29	−0.25
All Grades	−0.18	0.18	−1.03	−0.53	0.16	−0.79
Percent Free/Reduced Price Lunch
Less than 40%	−0.10	0.16	−0.61	−0.42	0.22	−0.42
40–60% (referent)
Greater than 60%	−0.14	0.14	−0.96	−0.41	0.14	−0.59
R-square (Within)	0.44
R-square (Between)	0.97

(D) = Task Demands; (R) = Resources; (S) = Situational Factors; SWPBIS = Schoolwide Positive Behavioral Interventions and Supports.

Staff mean scores per school (n = 574).

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

Conclusions

The present study examined contextual predictors of perceived organizational readiness among implementation team members across 40 rural Idaho schools. In line with prior theorizing of organizational readiness (Weiner, 2009), we hypothesized significant, positive associations between team members’ perceived organizational readiness and all predictors except for the index of risk factors, which was hypothesized to have a negative association. Overall, the findings suggest that team members who perceived their school to have more facilitative and supportive environments believed their teams and school staff were more prepared to implement evidence-based programming. We observed relationships with indicators for each of the task, resource, and situational factors, including greater belief in the intervention, system support, school protective factors, and school resources, each uniquely contributing to greater perceived organizational readiness among team members. The situational factors of system support and school protective factors were also influential at the individual level in the degree to which team members perceived readiness among the broader school staff. Team members also rated staff readiness higher for schools that reported greater than average leadership support for change.

Notably, some theorized predictors were not associated with how team members perceived their own or their school staff's readiness to implement evidence-based programming. Knowledge of the intervention had no observed effect on readiness, whether in the team or among school staff, after accounting for other contextual factors. In contrast to theories of readiness (e.g., Weiner, 2009), the results in practice suggest that respondents determined their team and staff's readiness more based on their perceptions of support more than on their own capacities, the personal resources at hand, or risk factors in the school environment. Although the variance at the school level was limited, school resources and transformational leadership estimated at the school level were also significant predictors of team readiness and perceived staff readiness, respectively. Analyzed holistically, respondents’ assessment of their team's and staff's levels of readiness appeared largely associated with the degree to which they both felt and experienced support and resources from the school, rather than with the challenges of implementing the intervention.

Implications for Practice

The motivation of implementation team members plays a key role in an organization's preparedness (Kincaid et al., 2007; Zhang et al., 2023). The present study assessed two important contributors to this motivation: knowledge of and belief in the intervention, with the latter demonstrating improved perceptions of their team's readiness to implement. This suggests that motivational support, such as training that focuses on attitudinal changes and specific implementation skills, may be an effective way to improve not only team readiness but ultimately program outcomes. Improving the motivation and attitudes of school members in implementing evidence-based universal programs has indeed been found to be an effective method for improving implementation fidelity in universal prevention practices and multi-tiered system of supports (Cook et al., 2015; Zhang et al., 2023).

Team members’ belief in their team's readiness to implement was strongly tied to the level of resources at the school and the situational supports they perceived from the school and the school system. Importantly, team members who perceived greater system support and protective factors than their fellow team members had stronger perceptions of both their own readiness and the readiness of the broader school staff. Prior research that contextualizes organizational readiness within the school implementation climate suggests that those responsible for implementation do so more effectively with an understanding and appreciation of the organization's priorities and purpose (Arthur et al., 2020; Weiner, Lewis, et al., 2008). Accordingly, building resources and a supportive environment from the system to the school would likely contribute to team members’ alignment with the school's priorities and mission. However, the availability of practical resources did not seem as a great factor in team members’ perceptions of staff readiness. Instead, a facilitative school and leadership environment was more relevant to building readiness among school staff. A stronger belief in an existing support infrastructure, including in extension beyond the school to the district or state level, was clearly an important factor in whether respondents felt their schools were prepared to implement evidence-based practices.

An important finding from the present study is that the perception of positive supports and the availability of school resources strongly outweighed perceived school risk factors. School administrators and district leaders invested in preparing schools to employ evidence-based practices could be efficient with their resources by highlighting available support and prioritizing infrastructure to foster connections to those resources, rather than by mitigating deficiencies. Implementing smaller-scale supports, such as creating more consistent and visible school-wide practices, and directing existing school and system resources to those implementing EBPs, would foster a belief among staff that their efforts are supported by administrators, contributing to a progressive staff environment that can more readily accept and apply new practices and likely withstand risks to their success. However, other practical limitations not assessed here, such as staff turnover and leadership buy-in, are major challenges to organizational readiness (Splett et al., 2022) that also must be addressed.

Implications for Research

A variety of nuanced definitions and measures of organizational readiness have been developed over the past few decades (Mueller et al., 2012; Newhouse, 2010; Scaccia et al., 2015; Shea et al., 2014; Stamatakis et al., 2012; Walker et al., 2020; Weiner, Amick, et al., 2008), each assessing a particular element or environment for readiness. A difference can also be drawn between general readiness to implement and readiness to implement specific evidence-based programs (Wanless & Domitrovich, 2015). In the present study, organizational readiness refers to team members’ perceptions of their team's preparedness and their school staff's capacity to implement a specific set of evidence-based practices. However, even this interpretation can be considered somewhat monolithic. For example, implementation teams typically include a diverse group of administrators, counselors, and teachers, who each bring a different perspective and skillset to implementation, as well as each having different levels of social capital and power within the organization. For each team member, organizational readiness is likely to have a different meaning (Hustus & Owens, 2018). Likewise, Weiner (2009) conceptualizes organizational readiness as a “shared team property,” for which group agreement is itself an indicator of readiness. Future research with larger samples should examine both inter- and intra-group agreement, particularly among diverse team members, as they relate to preparedness to implement EBPs in schools.

This study explored potential predictors of readiness relative to an individual-level theoretical conceptualization of organizational readiness. Within this framework, we examined only cross-sectional, linear relationships. Further, as the construct of organizational readiness is differentially defined by individual motivations and organization-level capacity, these different interpretations may change the directionality or orientation of the predictors examined here. For example, in describing readiness for implementation at the organizational level, Damschroder et al., (2009) identify leadership engagement and the availability of resources as two of its defining features, rather than the determinative constructs we associated with readiness for implementation at the individual level. We instead theorized these and related factors in relation to the psychological motivations and perceived capacity of individuals within schools. More complex frameworks exist (e.g., Burke & Litwin, 1992; Oterkiil & Ertesvåg, 2012) that conceptualize the multilevel complexity of readiness, but they often come at the expense of practical testability. Nevertheless, researchers may find it useful to examine their theorized relationships within these broader conceptualizations to identify reflexive or alternative pathways.

Strengths and Limitations

The present study benefitted from a large sample of rural K-12 schools, but results may not be generalizable to schools with other demographic characteristics. Schools in the present study were preparing to implement a specific universal evidence-based approach and may not reflect schools implementing other interventions, schools at earlier stages of adoption, or schools implementing targeted or intensive interventions only. Likewise, data were collected cross-sectionally from school members, so causal inference is limited. Subsequent examination with a more diverse population of schools, particularly when implementing other evidence-based practices, as well as using longitudinal data from more diverse sources, would help to confirm the factors predictive of organizational readiness. In addition, because of the nature of the implementation teams, representation from each school was inherently limited in size. This also precluded our ability to examine results relative to team members’ positions at the school. School administrators, who made up about 20% of our sample, may have had more optimistic perceptions of school preparedness and its orientation to evidence-based programming, which may be reflected in our results. Relatedly, demographic data were not collected for team members and thus were unaccounted for in the models. Lastly, Weiner (2009) argues that organizational readiness is most relevant when it illustrates mutual agreement among staff in their motivation to implement change. The focus of this study was on the individuals’ perceptions, which were necessarily assessed as the deviation from either the team or sample averages. Nevertheless, team interactions around some of these predictors may have influenced members’ perceptions. Further research is necessary to more readily understand the nature of implementation team dynamics and how these may influence perceptions of readiness.

Despite the centrality of organizational readiness to the implementation of evidence-based programming, its contributors remain vastly understudied in school contexts. This study provided some initial insights into factors that may improve readiness to implement among both team members and the broader school environment. Most important appears to be building observable linkages of support between the school system, school, and staff, with particular attention to reinforcing connections between those supports and implementation team members.

Supplemental Material

sj-docx-1-irp-10.1177_26334895261456680 - Supplemental material for Contextual Factors Associated With Readiness to Implement Universal Prevention Programs in Rural Schools

Supplemental material, sj-docx-1-irp-10.1177_26334895261456680 for Contextual Factors Associated With Readiness to Implement Universal Prevention Programs in Rural Schools by Christopher M. Fleming and Lindsey Turner in Implementation Research and Practice

Footnotes

ORCID iDs

Christopher M. Fleming

Lindsey Turner

Ethical Considerations

This research was approved by the Institutional Review Board at Boise State University (protocol number 101-SB17-207).

Consent to Participate

All participants provided informed consent prior to each survey.

Author Contributions

LT and CF contributed to conceptualization and design of the analyses; CF conducted analyses and drafted the manuscript; LT designed and implemented the study protocol and reviewed and edited the manuscript; both authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the National Institute of Justice, #2017-CK-BX-0021.

Declaration of Conflicting Interests

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

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available due to security provisions of the protocol approved by the institutional review board, but de-identified data may be available from the corresponding author on reasonable request.

Trial Registration

This research was prospectively registered on ClinicalTrials.gov (NCT03736395) on November 9, 2018, .

Supplemental Material

Supplemental material for this article is available online.

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