- Open Access
Testing a theory of strategic implementation leadership, implementation climate, and clinicians’ use of evidence-based practice: a 5-year panel analysis
Implementation Science volume 15, Article number: 10 (2020)
Implementation theory suggests that first-level leaders, sometimes referred to as middle managers, can increase clinicians’ use of evidence-based practice (EBP) in healthcare settings by enacting specific leadership behaviors (i.e., proactive, knowledgeable, supportive, perseverant with regard to implementation) that develop an EBP implementation climate within the organization; however, longitudinal and quasi-experimental studies are needed to test this hypothesis.
Using data collected at three waves over a 5-year period from a panel of 30 outpatient children’s mental health clinics employing 496 clinicians, we conducted a quasi-experimental difference-in-differences study to test whether within-organization change in implementation leadership predicted within-organization change in EBP implementation climate, and whether change in EBP implementation climate predicted within-organization change in clinicians’ use of EBP. At each wave, clinicians reported on their first-level leaders’ implementation leadership, their organization’s EBP implementation climate, and their use of both EBP and non-EBP psychotherapy techniques for childhood psychiatric disorders. Hypotheses were tested using econometric two-way fixed effects regression models at the organization level which controlled for all stable organizational characteristics, population trends in the outcomes over time, and time-varying covariates.
Organizations that improved from low to high levels of implementation leadership experienced significantly greater increases in their level of EBP implementation climate (d = .92, p = .017) and within-organization increases in implementation leadership accounted for 11% of the variance in improvement in EBP implementation climate beyond all other covariates. In turn, organizations that improved from low to high levels of EBP implementation climate experienced significantly greater increases in their clinicians’ average EBP use (d = .55, p = .007) and within-organization improvement in EBP implementation climate accounted for 14% of the variance in increased clinician EBP use. Mediation analyses indicated that improvement in implementation leadership had a significant indirect effect on clinicians’ EBP use via improvement in EBP implementation climate (d = .26, 95% CI [.02 to .59]).
When first-level leaders increase their frequency of implementation leadership behaviors, organizational EBP implementation climate improves, which in turn contributes to increased EBP use by clinicians. Trials are needed to test strategies that target this implementation leadership–EBP implementation climate mechanism.
Increasing the delivery of evidence-based practices (EBP) in community settings is a major goal of efforts to transform health and behavioral health care delivery globally given widespread evidence of research-to-practice gaps in the quality and effectiveness of health services [1,2,3,4]. While implementation science has contributed myriad frameworks, methods, and outcomes over the past two decades , there is an increasing call for understanding the mechanisms or levers that can be targeted to improve the adoption, implementation, and sustainment of EBPs in health and behavioral healthcare systems [6,7,8]. Mechanisms represent causal processes through which an outcome occurs [9,10,11]. To date, many implementation frameworks have proposed variables that are conceptually related to EBP implementation [12, 13], and investigators have begun to validate measures of these constructs  and study how these constructs relate to each other and to implementation outcomes in cross-sectional studies . An essential next step is to test whether changes in these variables contribute to changes in implementation outcomes using experimental, longitudinal, and/or quasi-experimental designs that incorporate rigorous controls [6, 9]. In this study, we advance understanding of the mechanisms that influence EBP implementation by examining how changes in organizational leadership and climate influence clinicians’ use of EBP across a 5-year period in a large sample of community-based behavioral health agencies that serve youth .
The role of first-level leaders in EBP implementation
Implementation frameworks posit the importance of organizational leaders and their use of specific leadership behaviors as potential mechanisms to improve EBP implementation in healthcare settings [17,18,19,20]. These frameworks converge on the importance of leaders at multiple levels—from executives who make decisions about whether to implement an EBP to first-level leaders who directly supervise clinicians [21,22,23]—as well as on the importance of aligning leadership across levels [24,25,26]. Furthermore, many frameworks suggest the unique importance of first-level leaders, also known as middle managers, who directly supervise frontline clinicians [21, 27]. First-level leaders are believed to be important for supporting EBP implementation because they have frequent interpersonal contact with clinicians, they play a prominent role in supervision and guiding clinical care, and they form a bridge between executives who often make decisions about the adoption of EBPs and clinicians who are tasked with implementing EBPs with clients [28, 29].
In this study, we test a theory of implementation leadership proposed by Aarons et al. [24, 27, 30] which posits specific hypotheses regarding the types of behaviors first-level leaders can use to influence clinicians’ EBP implementation behavior and the specific mechanism through which these behaviors influence clinicians’ practice. As is shown in Fig. 1, this theory suggests that when first-level leaders use a specific set of behaviors referred to as ‘implementation leadership,’ they will improve clinicians’ EBP implementation by creating an EBP implementation climate within the organization that conveys strong expectations, support, and rewards for the use of EBP [31,32,33]. In turn, the creation of an EBP implementation climate within the organization serves as the most proximal, salient, and powerful organization-level antecedent to clinicians’ EBP implementation behavior .
EBP implementation climate, defined as the extent to which employees share perceptions that the adoption and implementation of EBP is expected, supported, and rewarded within their organization [31, 34, 35], is hypothesized to be important for shaping clinicians’ implementation behavior in part due to the inherent difficulty in closely monitoring and controlling the processes of healthcare delivery . In contrast to manufacturing and production processes, which are highly routinized, predictable, and observable, the process of delivering behavioral healthcare is non-routinized and often unpredictable, simultaneously produced by clinicians and consumed by patients, and occurs in private encounters . These characteristics make it difficult and expensive to ensure EBPs are delivered with fidelity during every clinical encounter. As an alternative, organizational leaders can align organizational policies, procedures, practices, incentives, and supports to create an EBP implementation climate that shifts clinicians’ attitudes and motivation toward the effective use of EBPs in their practice [31, 33, 38]. When clinician perceptions of EBP implementation climate are high, it signals a shared belief that EBP use is a true and lasting priority for the organization rather than a passing trend that can be ignored. Cross-sectional studies demonstrate that healthcare organizations vary significantly in their levels of EBP implementation climate and that clinicians in organizations with higher levels of EBP implementation climate have better attitudes toward EBPs and use them to a greater extent than those in organizations with low levels of EBP implementation climate [39,40,41,42]. Data are lacking, however, on whether and how EBP implementation climate changes over time and how these changes relate to change in clinicians’ behavior.
First-level leaders are posited to play a key role in shaping EBP implementation climate because they are the most salient and immediate representation of the organization’s expectations, policies, procedures, and practices which form a basis for clinicians’ climate perceptions . From an interactional standpoint, first-level leaders use both general transformational leadership and strategically focused implementation leadership behaviors to influence clinicians’ perceptions of EBP implementation climate . As described by the full-range leadership model, transformational leadership refers to a set of general leader behaviors that are applicable across many settings where the leader wishes to inspire and motivate followers to pursue an ideal or a course of action . Leaders who are transformational serve as idealized role-models of the qualities and behaviors they hope their followers will enact. They present a value-based vision that inspires action and they engage their followers’ intellect in co-resolving critical challenges within the context of supportive relationships . Within Aaron et al.’s model [24, 27, 30], these general leadership behaviors form a necessary but not sufficient foundation on which the leader builds to improve EBP implementation.
In contrast to the generalized and broadly applicable transformational leadership behaviors, implementation leadership refers to strategically focused leader behaviors that reflect the leader’s commitment to, support for, and perseverance during EBP implementation [27, 36, 44]. Whereas transformational leadership provides a foundation of trust for the leader-clinician relationship, implementation leadership focuses that relationship, in part, on integrating the use of EBP in clinical practice. Drawing on the concept of climate embedding mechanisms, which represent behaviors leaders use to create a specific type of climate within their organization [24, 45], Aarons et al.  posit that implementation leadership is exhibited through behaviors of (a) proactively planning for and removing obstacles to implementation, (b) demonstrating and applying knowledge regarding the specific EBP being implemented, (c) supporting and appreciating followers’ efforts to implement the EBP, and (d) responding effectively to challenges and persevering with EBP implementation even when it becomes difficult . These implementation-focused leadership behaviors complement general transformational leadership; together, the two types of leadership contribute to a positive leader-follower relationship that is focused on the strategic imperative of effectively integrating EBP into care delivery . Cross-sectional studies have shown that higher levels of implementation leadership among first-level leaders are linked to higher levels of EBP implementation climate [40, 46, 47] and superior clinician attitudes toward EBP ; however, we are unaware of any longitudinal studies that examine how implementation leadership changes over time or that link these changes to changes in EBP implementation climate.
In order for implementation leadership to serve as a mechanism that influences clinicians’ EBP implementation behaviors, it must be true that implementation leadership is malleable, and that changes in implementation leadership contribute to change in clinicians’ behavior either directly or indirectly (e.g., via change in EBP implementation climate) [6, 7]. In this study, we address both of these issues by examining how, and if, implementation leadership changes naturalistically over time within behavioral healthcare organizations, and by testing whether these changes are associated with changes in an organization’s EBP implementation climate and clinicians’ EBP use. In addition, we test whether Aarons et al.’s [24, 27, 30] theory has discriminant validity by examining whether changes in implementation leadership and EBP implementation climate are related to change in clinicians’ non-EBP practice behaviors. An important premise of the theory is that implementation leadership and EBP implementation climate have specific and narrowly defined effects on EBP use. If changes in implementation leadership and EBP implementation climate predict change in other, non-EBP practice behaviors, this would call into question the validity of the theory or the validity of the measures used to test the theory.
Hypothesis 1: Within-organization increases in implementation leadership by first-level leaders will predict within-organization increases in the level of EBP implementation climate.
Hypothesis 2: Within-organization increases in EBP implementation climate will predict within-organization increases in clinicians’ EBP use.
Hypothesis 3:Within-organization increases in EBP implementation climate will not predict within-organization increases in clinicians’ use of non-EBP techniques (evidence of discriminant validity).
Hypothesis 4: Within-organization increases in EBP implementation leadership will indirectly increase clinicians’ EBP use via increases in the level of EBP implementation climate (i.e., mediation).
Together, these hypotheses (a) test the implementation leadership–EBP implementation climate mechanism described above and shown in Fig. 1, and (b) provide discriminant validity evidence for the model by testing whether hypothesized changes in self-reported technique use were specific to EBP or occurred for both EBP and non-EBP techniques.
Study setting and design
Using data collected at three waves over a 5-year period from a panel of 30 outpatient children’s mental health clinics , we conducted a quasi-experimental difference-in-differences study [49, 50], incorporating econometric two-way fixed effects regression models , to test whether within-organization changes in implementation leadership were associated with changes in EBP implementation climate and ultimately with changes in clinicians’ EBP use. Difference-in-difference designs that use data from a longitudinal panel are the gold standard quasi-experimental approach because they permit investigators to isolate the relationships between within-unit change in presumed antecedents and consequents by eliminating all stable, between-unit confounds that may explain variation in the outcome of interest and by controlling for population trends in the consequents over time .
In a classic difference-in-differences design, longitudinal data are collected on a set of units that never experience the exposure (i.e., an organization that has continually low implementation leadership/ EBP implementation climate) and on a set of units that experience the exposure after a baseline period of time (i.e., an organization where implementation leadership/ EBP implementation climate shifts from low to high). Under the common trend assumption, the effect of the exposure is estimated as the difference between the change in the outcome that occurs in the exposed group less the change that occurs in the unexposed group (i.e., difference-in-differences) . This approach can be generalized to studies in which units experience the exposure at different times (i.e., changes in implementation leadership/ EBP implementation climate occur at different waves), and move in and out of a state of exposure, through the use of two-way fixed effects regression models . In addition, two-way fixed effects regression models can be used to estimate relationships between within-unit change in continuous antecedent and consequent variables ; in these analyses, each unit (i.e., each organization) serves as its own control and the effects that are estimated between continuous variables represent within-unit effects . In this study, we applied these approaches to examine the relationships between organization-level changes in antecedents and consequents within 30 outpatient children’s mental health clinics measured at three waves across a 5-year period. We followed the STROBE checklist for reporting elements of this longitudinal, quasi-experimental study  (see Additional file 1).
The organizations in this study were embedded within a publicly funded behavioral health system and full details of the larger study, including its overall design and the system-level context within which it occurred are published elsewhere [16, 53]. In brief, beginning in 2013, the City of Philadelphia in the USA launched the Evidence Based Practice and Innovation Center (EPIC) to improve EBP implementation in its Medicaid-funded behavioral health provider network which serves over 600,000 persons annually . EPIC supports EBP implementation on a system-wide basis through a coordinated set of policy, fiscal, and technical assistance activities which are described elsewhere . During the study period, EPIC supported the implementation of cognitive behavioral psychotherapies with strong empirical support for treating a range of psychiatric disorders, including cognitive therapy, , prolonged exposure, , trauma-focused cognitive-behavioral therapy , dialectical behavior therapy , and parent-child interaction therapy .
Recognizing the opportunity this system-level effort presented, our research team partnered with the City of Philadelphia to assess changes in clinicians’ use of EBP during 5 years and to examine how these changes related to changes in organizational characteristics presumed to influence EBP implementation . Whereas previous work evaluated the effectiveness of EPIC , the focus of this study was to test the theoretical model shown in Fig. 1. The 5-year study period began immediately prior to the launch of EPIC in 2013 and continued through 2017. The present study uses data from all three waves including assessments completed at baseline (2013), 2-year follow-up (2015), and 5-year follow-up (2017).
The study incorporated two levels of sampling—organizations and clinicians. At the organization level, beginning in 2012, we purposively sampled publically funded organizations that deliver outpatient mental health services to Medicaid-eligible youth in Philadelphia. Given that Philadelphia has a single payer system (Community Behavioral Health; CBH) for public behavioral health services, we obtained a list from the payer of all organizations that had submitted a claim in 2011–2012. There were over 100 organizations delivering outpatient services to youth. Our intention was to use purposive sampling to generate a representative sample of the organizations that served the largest number of youth in the system. We selected the first 29 organizations as our population of interest because together they serve approximately 80% of youth receiving publically funded behavioral health care. The majority of the remaining organizations in the system were very small and did not employ many clinicians and/or see many youth. The organizations that we recruited were geographically spread across Philadelphia county and ranged in size with regard to number of youth served. Over the course of the study, we enrolled 22 of the 29 organizations (76%). Some organizations had multiple sites with distinct leadership structures and operations at each site; these were treated as separate units in the analysis for a total of N = 31 sites, which we refer to as organizations in what follows. Of the 31 organizations that participated in the study across the 5-year study period, 18 participated in three waves, nine participated in two waves, and four participated in one wave. One organization was a multivariate outlier on study measures at wave 3, resulting in a final analytic sample of N = 30 organizations with a total of k = 73 organization-level observations across three waves (average of 2.4 observations per organization).
The second level of sampling included all clinicians who worked with youth within each organization at each wave. Specifically, with the permission of organizational leaders, researchers scheduled group meetings with all clinicians working within the organizations that delivered youth outpatient services, during which the research team presented the study, obtained written informed consent, and collected measures onsite. The only inclusion criterion was that clinicians deliver behavioral health services to youth (clients under age 18) via the outpatient program. We did not exclude any clinicians meeting this criterion and included clinicians-in-training (e.g., interns).
In total, 496 clinicians were included in the analytic sample of 30 organizations. Within this group, 387 clinicians (78%) provided data once; 94 clinicians (19%) provided data twice, and 15 clinicians (3%) provided data three times. The majority of clinicians were female (81%) and came from diverse racial and ethnic backgrounds (47% white, 27% African American, 15% ‘Other Race,’ 6% Asian, < 1% Native Hawaiian or Other Pacific Islander or American Indian or Alaska Native, 19% Latino). The average age was 37.3 years (SD = 11.41) with an average of 8.6 years (SD = 8.5) of experience in human services and 2.7 years (SD = 3.9) in their current agency. Ten percent of clinicians had a doctoral degree and the remainder had master’s or bachelor’s degrees. Clinicians’ demographic characteristics did not vary by wave (all p’s > .10). The two-level sampling approach allowed us to examine changes in implementation leadership, EBP implementation climate, and EBP use at the organization level over time without assuming that individual clinicians were the same at each wave.
Clinicians completed study questionnaires in 2013, 2015, and 2017 during a 1- to 1.5-h meeting with researchers at their organization during regular work hours. In order to assure confidentiality and minimize demand characteristics, organizational leaders were not present. Questionnaires addressed clinicians’ use of psychotherapy techniques with a representative client, first-level leader implementation behavior, clinician perceptions of their organization’s EBP implementation climate and general organizational climate, as well as clinician professional and demographic characteristics. Questionnaires were returned directly to researchers and clinicians were assured that organizational leaders would not have access to their individual-level data. Participants received $50 USD for their time. All study procedures were approved by the City of Philadelphia Institutional Review Board and the University of Pennsylvania Institutional Review Board.
Clinicians’ self-reported use of EBP was measured using the 33-item cognitive-behavioral therapy (CBT) subscale of the Therapy Procedures Checklist-Family Revised (TPC-FR) . Scores on this scale indicate the frequency with which clinicians use cognitive-behavioral techniques with a representative client. Prior research supports the TPC-FR’s test-retest reliability, criterion-related validity, and within-therapist sensitivity to change [61, 62]. The measure presents a list of specific psychotherapy techniques derived from three well-established models (CBT, family therapy, and psychodynamic therapy) and clinicians indicate the extent to which they use each technique with a current client who is representative of their larger caseload. Item responses are made on a continuum ranging from 1 (rarely) to 5 (most of the time). We used the CBT subscale as the primary criterion variable in this study because of the strong empirical support for the effectiveness of CBT in the treatment of youth psychiatric disorders [63,64,65,66,67] and because the larger system initiative supported this model. Coefficient alpha for this scale was α = .93 in the sample.
In order to provide discriminant validity evidence for our theoretical model, we also used the 16-item psychodynamic subscale of the TPC-FR as an outcome indicative of clinicians’ use of non-EBP techniques for hypothesis 3. Psychodynamic techniques have weaker empirical support for the treatment of psychiatric disorders among youth [68, 69]. Coefficient alpha was α = .85 in the sample.
In order to align our levels of theory and analysis, we aggregated (i.e., averaged) clinicians’ responses on the CBT and psychodynamic subscales to the organization level at each wave. Thus, our dependent variables represented the average frequency with which clinicians used cognitive-behavioral (i.e., EBP) or psychodynamic techniques (i.e., non-EBP) with a representative client at each wave [70, 71].
Clinicians rated the implementation leadership of their first-level leader (i.e., their direct supervisor) using the 12-item Implementation Leadership Scale (ILS) . This scale assesses a leader’s behavior with regard to (a) proactiveness in promoting EBP implementation, (b) knowledge of EBP and using this to support clinicians, (c) daily support of EBP implementation, and (d) perseverance through the ups and downs of EBP implementation. Responses are made on a 0 (not at all) to 4 (very great extent) scale and a total score is derived by averaging the items. Psychometric studies indicate scores on the ILS exhibit excellent internal consistency and convergent and discriminant validity . Coefficient alpha in the sample was α = .98.
Clinicians’ perceptions of their organization’s EBP implementation climate were measured using the total score of the 18-item Implementation Climate Scale (ICS) . This scale addresses six subdomains which capture the extent to which an organization focuses on EBP, provides educational support for EBP, recognizes clinicians for excellence in EBP, rewards clinicians for demonstrating expertise in EBP, selects clinicians based on their EBP acumen, and selects clinicians for general openness. Responses are made on a 0 (Not at All) to 4 (A Very Great Extent) scale and a total score is calculated as the mean across all items. Prior research supports the ICS’s structural validity, total score reliability, and convergent and discriminant validity [31, 41, 46]. Coefficient alpha for this scale was α = .94 in the sample.
Two-way fixed effects regression models control for all stable organizational characteristics and for population trends in the outcome over time; however, they do not control for potential time-varying confounds within organizations from wave-to-wave (e.g., wave-to-wave change in general leadership or climate) . To address this, we included the following variables as covariates in all analyses.
Molar organizational climate is defined as clinicians’ shared perceptions of the overall impact of the work environment on their personal well-being; that is, whether the work environment is a ‘good’ or ‘bad’ place to work . We assessed it using the well-established, 15-item functionality scale of the Organizational Social Context (OSC) measure to account for the general work environment and because prior research indicates that this characteristic is related to clinicians’ use of EBP [53, 73, 74]. Prior research supports this scale’s structural and criterion-related validity [73, 75, 76]. Responses are made on a five-point scale from 1 (Never) to 5 (Always). Coefficient alpha was α = .92 in the sample.
First-level leaders’ transformational leadership was included in the models in order to isolate the effects of implementation leadership (vs. transformational leadership) on EBP implementation climate and because prior research has linked transformational leadership to EBP implementation climate, clinicians’ attitudes toward EBP, and EBP adoption and sustainment [42, 77,78,79,80]. Clinicians rated the extent to which their first-level leaders exhibited transformational leadership behaviors using the Multifactor Leadership Questionnaire [81, 82], which is one of the most widely used measures of transformational leadership and has excellent psychometric properties . Consistent with prior research, we used the transformational leadership total score (averaged on a 5-point scale from Not at all to Frequently, if not always) which incorporates four subscales: Idealized Influence, Inspirational Motivation, Intellectual Stimulation, and Individual Consideration (20 items, α = .97).
We included a single, time-varying, workforce variable—clinicians’ average years of experience—in our models based on preliminary bivariate analyses which showed that this was the only time-varying workforce characteristic associated with change in clinicians’ EBP use in our sample (B = .02, SE = .01, p = .031). No other time-varying workforce characteristics, including clinicians’ average attitudes toward EBP as measured using the Evidence-Based Practice Attitudes Scale , were associated with EBP use (all p’s > .10) and there was no evidence that first-level leader turnover predicted EBP use (p = .322). Clinicians reported on their years of experience working as a clinician and this variable was averaged to the organization-level at each wave to represent the average experience of the organization’s workforce at each wave.
Consistent with best practices [6, 70, 85], we generated organization-level values for implementation leadership, EBP implementation climate, molar organizational climate, and transformational leadership by aggregating (i.e., averaging) clinicians’ individual responses to the organization level on these respective scales. The validity of these compositional variables was supported by high levels of inter-rater agreement within each organization, as measured using the awg(j) statistic; all awg(j) values were above the recommended cutoff of .7 in our sample [86, 87].
In order to take full advantage of the data structure, we conducted two complementary sets of analyses that tested hypotheses 1–4. Both sets of analyses used econometric two-way fixed effects regression models , also referred to as panel linear models , at the organization level. First, following Wing et al.  and reflecting a traditional generalized difference-in-differences design, we categorized the implementation leadership and EBP implementation climate within each organization at each wave as either high or low based on a median split and used these dichotomous indicators as measures of exposure. The two-way fixed effects regression models were specified as:
where Yit represents the outcome for organization i at time t, Oi represents a set of dummy variables that control for the combined effects of all stable organizational characteristics, Tt represents a set of dummy variables that capture the combined effects of population trends or shocks that affect all organizations at time t, Zit represents a vector of time-varying covariates, and Xit represents the dichotomous exposure variable indicating whether the organization experienced high or low implementation leadership or EBP implementation climate at time t. The β4 coefficient captures the exposure effect, that is, the mean improvement in the outcome attributable to an organization shifting from a low to a high level of either implementation leadership (in model 1) or EBP implementation climate (in model 2). Effect sizes for these models are expressed as Cohen’s d  which captures the conditional, standardized mean difference in change between organizations that shifted from low to high levels of implementation leadership (or EBP implementation climate) versus those that did not.
Second, we ran the same models but included implementation leadership and EBP implementation climate as continuous variables (rather than dichotomous indicators) in the analyses. The beta coefficients from these two-way fixed effects regression models represent the effect of within-organization change in each predictor on within-organization change in each outcome, controlling for all stable between-organization differences, population trends in the outcome over time, and the other time-varying predictors in the model. Effect sizes for these models are expressed as an incremental R-squared; that is, the percentage of variance in within-organization change in the outcome accounted for by the focal predictor over-and-above all other predictors in the model. All analyses were implemented in R using the plm package . Following best practices, missing waves of data were handled using maximum likelihood estimation . Following estimation of all models, we examined residual plots and other diagnostics to confirm the tenability of model assumptions.
We used the product of coefficients approach, in conjunction with the joint significance test [91, 92] and asymmetric 95% Monte Carlo confidence intervals, to test our mediation hypothesis (hypothesis 4) [93, 94]. Under this approach, regression analyses are used to estimate the two paths shown in Fig. 1 that link the independent variable to the mediator (‘path a’) and the mediator to the dependent variable, controlling for the independent variable (‘path b’). The product of these path estimates (i.e., a*b) quantifies the indirect or mediation effect . The statistical significance of the mediation effect is tested using (1) the joint significance test, which represents a null hypothesis significance testing approach, and (2) asymmetric 95% confidence intervals developed via Monte Carlo simulation methods with 100,000 replications [93, 96].
Table 1 presents descriptive statistics for all study variables at wave 1 as well as the average within-organization change and variation in within-organization change from wave to wave. Figure 2 shows how the primary antecedent and consequent variables changed within organizations over time. In order to test whether there was significant variation across organizations in how EBP use, implementation leadership, and EBP implementation climate changed from wave to wave, we conducted a series of one-sample t tests. These tests compared the mean absolute value of within-organization change from wave 1 to wave 2 (and wave 2 to wave 3) to a population value of zero. All t tests were statistically significant (all p’s < .001), confirming that the absolute value of within-organization change from wave to wave was significantly different from zero for our primary antecedent and consequent variables. Furthermore, the Cohen’s d effect size for these (absolute value) within-organization changes were d = .60 for EBP use, d = .75 for implementation leadership, and d = .74 for implementation climate, representing medium effects . Table 1 also shows the percentage of organizations that experienced a moderate change (defined as a > .5 standard deviation change in either direction) from wave to wave on each variable. These analyses confirm that there were statistically significant and substantively meaningful within-organization changes in the primary antecedent and consequent variables of interest during the study period.
Effect of implementation leadership on EBP implementation climate
Table 2 presents the difference-in-differences analyses testing hypotheses 1–3; Table 3 presents similar analyses but includes implementation leadership and EBP implementation climate as continuous variables.
Hypothesis 1 was supported by both sets of analyses. As is shown in Table 2, organizations that improved from low to high levels of implementation leadership experienced significantly greater increases in their level of EBP implementation climate compared to organizations that did not change in their level of implementation leadership (B = .48, SE = .19, p = .017). This represents a large effect of d = .92 according to criteria suggested by Cohen .
As is shown in Table 3, similar results were obtained in the analyses that included implementation leadership and EBP implementation climate as continuous variables: within-organization increases in implementation leadership predicted within-organization improvement in the level of EBP implementation climate (B = .44, SE = .14, p = .004), accounting for 11% of the variance beyond all other covariates (i.e., R2incremental = .11). This represents a moderate effect size .
Effect of EBP implementation climate on clinicians’ EBP use
Hypothesis 2 was also supported by both sets of analyses (see Tables 2 and 3). As is shown in Table 2, organizations that improved from low to high levels of EBP implementation climate experienced significantly greater increases in their clinicians’ average use of EBP compared to organizations that experienced no change in EBP implementation climate (B = .23, SE = .08, p = .007), controlling for implementation leadership and all other covariates. This represented a medium effect size of d = .55.
Table 3 shows that similar results were obtained in the analyses that included implementation leadership and EBP implementation climate as continuous variables: within-organization improvement in EBP implementation climate predicted within-organization increases in clinicians’ use of EBP (B = .36, SE = .13, p = .009), controlling for change in implementation leadership and all other covariates. This represented a moderate effect size of R2incremental = .14 . Results of these analyses also indicated that increases in clinicians’ average years of experience predicted increased EBP use (B = .03, SE = .01, p = .011).
Effect of EBP implementation climate on clinicians’ use of non-EBP
Hypothesis 3 was designed to provide discriminant validity evidence for the theorized model and it was supported by both sets of analyses (see Tables 2 and 3). Focusing on the continuous variable analyses in Table 3, within-organization change in EBP implementation climate was not related to within-organization change in clinicians’ use of non-EBP (B = .10, SE = .13, p = .424). The only significant predictor in this model was clinicians’ average years of experience (B = .02, SE = .01, p = .030).
Indirect effect of implementation leadership on clinicians’ EBP use via EBP implementation climate
Hypothesis 4 tested the full mediation model and it was supported by the results of the joint significance test and by the asymmetric 95% confidence intervals in both sets of analyses. As described above, organizations that improved from low to high levels of implementation leadership experienced greater increases in their level of EBP implementation climate compared to organizations that did not improve in implementation leadership (see Table 2, model 1: B = .48, SE = .19, p = .017); and, organizations that improved from low to high levels of EBP implementation climate experienced greater increases in clinicians’ use of EBP compared to organizations that did not improve in EBP implementation climate, while controlling for implementation leadership (see Table 2, model 2: B = .23, SE = .08, p = .007). Based on these analyses, we reject the null hypothesis of the joint significance test and conclude that there is positive evidence for the indirect effect of implementation leadership on clinicians’ EBP use via improvement in EBP implementation climate. Furthermore, the Monte Carlo 95% confidence interval for this indirect effect did not include zero (a*b = .11, 95% CI = .01 to .25), providing additional evidence that increases in implementation leadership had a significant indirect effect on increases in clinicians’ EBP use via improvement in EBP implementation climate. Similar results were found for the analyses that included implementation leadership and EBP implementation climate as continuous variables (see Table 3; a*b = .16, 95% CI = .03 to .33).
This study advances implementation theory and practice by providing the first mechanistic test of the hypothesis that first-level leaders can improve EBP implementation in healthcare settings through the use of specific implementation leadership behaviors that create an EBP implementation climate within their organization. The study validates the theory of first-level implementation leadership proposed by Aarons and colleagues  by supporting the hypothesis that increased frequency of implementation leadership behaviors by first-level leaders contributes to moderate to large improvements in EBP implementation climate and that, in turn, improved EBP implementation climate contributes to moderate increases in self-reported EBP use by clinicians. Further, the study provides evidence of discriminant validity, such that these relationships were specific to the targeted implementation behaviors and did not apply to non-targeted, non-EBP behaviors. That these hypotheses were tested within a longitudinal, difference-in-differences design incorporating 30 organizations measured across a 5-year period allows us to make the strongest possible inferences about the relationships between these variables short of manipulating them in a randomized experiment [49, 51]. To our knowledge, this study is among the first to use a rigorous methodological approach  to support a mechanistic understanding of the relationship between constructs from leading implementation science frameworks [17, 18] and theory [19, 25, 26, 28]. As such, it represents a critical step forward in advancing recommendations for rigorous work testing mechanisms and causal theory in implementation science [7, 8, 97].
Our findings confirm the hypothesis, based on theory and previous cross-sectional research, that first-level leaders play a critical role in EBP implementation and should be a target of implementation strategies [24, 26,27,28]. The importance of first-level leaders for creating an EBP implementation climate is likely due to their frequent interpersonal contact with frontline clinicians, their role as supervisors, and their status as a bridge between executives, who make decisions about specific EBP adoption, and clinicians who are tasked with implementing EBPs with clients [19, 25, 26]. Consistent with Aarons and colleagues’ theory [30, 98], our results indicate that first-level leaders bridge the gap in part by creating an EBP implementation climate that conveys high expectations, support, and rewards for the effective use of EBPs in clinical encounters. This study provides an actionable and important advance in our understanding of how to improve EBP implementation in healthcare settings by identifying specific implementation leadership behaviors that contribute to the formation of an EBP implementation climate and improved EBP use.
The implementation leadership behaviors described by Aarons et al.  (i.e., proactive, knowledgeable, supportive, and perseverant) have similarities to other implementation leadership theories, such as the theory of middle managers proposed by Birken et al.  and the concept of clinical champions , and further work is needed to clarify where these theories converge and where they make unique contributions. For example, both Birken et al. and Aarons et al. posit that first-level leaders influence EBP implementation by enacting behaviors that contribute to a positive EBP implementation climate within the organization. They differ, however, in framing these behaviors as climate embedding mechanisms (i.e., Aarons et al. ) versus commitment to the innovation and bridging communication gaps (Birken et al. ). Future research should clarify the extent to which the constructs from these theories represent complementary and potentially overlapping manifestations of the same underlying set of constructs versus unique approaches to conceptualizing implementation leadership. Research is also needed to develop validated measures of the constructs from Birken et al.’s theory .
Results of this study indicate there are important within-organization changes in implementation leadership, EBP implementation climate, and clinicians’ use of EBP across time even though the mean of these changes is sometimes close to zero because some organizations change in a positive direction and others in a negative direction. That some organizations exhibited improvement in implementation leadership and climate while others exhibited deterioration or no change, despite their shared exposure to the same outer context, underscores the mutability of inner context factors and their potential role in shaping EBP implementation [11, 12]. The implications of this for practice are that leaders can change their organizational contexts through the use of specific behaviors and that these changes can make a difference in clinicians’ behavior. From a research standpoint, these findings imply that implementation leadership and EBP implementation climate are mutable targets that could serve as the focus for implementation strategies designed to improve EBP implementation.
The next step in this line of research includes confirmative comparative effectiveness trials to test implementation strategies—such as the Leadership and Organizational Change for Implementation strategy (LOCI)  or the Training in Implementation Practice Leadership (TRIPLE)  strategy—that target the implementation leadership–EBP implementation climate mechanism identified in this study. The LOCI intervention [30, 98], which targets implementation leadership and EBP implementation climate through training and coaching for first-level leaders along with consultation with executives, has shown promise in pilot research and is currently under study in a number of settings [30, 101, 102]. The TRIPLE intervention aims to build leader skill in assessing quality of current service delivery; identifying appropriate and feasible EBPs; developing support for EBP use; assessing and increasing stakeholder engagement in improving practice; and identifying and using data to monitor quality and lead practice change. An initial pre-post evaluation of TRIPLE with first-level leaders found the program led to improvements in implementation leadership and EBP implementation climate . If these strategies are found to be effective across settings, they could present a meaningful approach to influencing implementation change. However, there are a number of questions that remain to be answered, specifically with regard to dismantling these approaches  to better understand the relative contribution of the focus on leadership and EBP implementation climate versus individual clinician attitudinal and motivation change, as well as questions of cost-effectiveness.
Study strengths include the use of a rigorous quasi-experimental design, the development of discriminant validity evidence with regard to the outcome (i.e., EBP vs. non-EBP techniques), and specification and testing of a robust theoretical model within a large longitudinal sample. However, several limitations should be noted. First, the study utilized self-reported use of EBP which does not always correlate strongly with actual behavior ; future studies that utilize observational metrics of fidelity would increase rigor. Second, while we had a relatively large and diverse sample of 30 organizations incorporating 496 clinicians, this work was conducted within a single system that was actively supporting EBP implementation among providers and thus may not generalize to other systems. Useful next steps include the replication of this study in different healthcare systems in order to test the generalizability of the results and the generalizability of the theory. Third, this study was observational in nature; because we did not experimentally manipulate variables, we cannot make causal inferences. Fourth, common method variance cannot be ruled out as an explanation for our study findings, although we included several rigorous controls and provided discriminant validity evidence with regard to the outcome. Finally, these results are likely most reflective of larger organizations rather than single-clinician providers of therapy services. Further, smaller organizations may play a vital role in culturally specific or niche service providers.
This study advances a mechanistic understanding of the relationships between implementation leadership, EBP implementation climate, and self-reported clinician EBP use, thus offering important targets for improving EBP implementation in clinical practice settings.
Availability of data and materials
RB and NW had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Requests for access to deidentified data can be sent to the Penn ALACRITY Data Sharing Committee by contacting Senior Research coordinator, Ms. Kelly Zentgraf at email@example.com, 3535 Market Street, 3rd Floor, Philadelphia, PA 19107, 215-746-6038.
Cognitive behavioral therapy
Evidence-Based Practice and Innovation Center
Implementation Climate Scale
Implementation Leadership Scale
Leadership and organizational change
Organizational social context
Therapy Procedures Checklist-Family Revised
Training in Implementation Practice Leadership
Insel TR. Translating scientific opportunity into public health impact: a strategic plan for research on mental illness. Arch Gen Psychiatry. 2009;66(2):128–33.
Collins PY, Patel V, Joestl SS, March D, Insel TR, Daar AS, et al. Grand challenges in global mental health. Nature. 2011;475(7354):27–30.
Garland AF, Haine-Schlagel R, Brookman-Frazee L, Baker-Ericzen M, Trask E, Fawley-King K. Improving community-based mental health care for children: translating knowledge into action. Admin Pol Ment Health. 2013;40(1):6–22.
Weisz JR. Agenda for child and adolescent psychotherapy research: on the need to put science into practice. Arch Gen Psychiatry. 2000;57(9):837–8.
Wensing M, Grol R. Knowledge translation in health: how implementation science could contribute more. BMC Med. 2019;17 https://doi.org/10.1186/s12916-019-1322-9.
Williams NJ. Multilevel mechanisms of implementation strategies in mental health: integrating theory, research, and practice. Admin Pol Ment Health. 2016;43(5):783–98.
Lewis CC, Klasnja P, Powell BJ, Tuzzio L, Jones S, Walsh-Bailey C, et al. From classification to causality: advancing understanding of mechanisms of change in implementation science. Front Public Health. 2018;6 https://doi.org/10.3389/fpubh.2018.00136.
Williams NJ, Beidas RS. Annual research review: the state of implementation science in child psychology and psychiatry: a review and suggestions to advance the field. J Child Psychol Psychiatry. 2019;60(4):430–50.
Kazdin AE. Mediators and mechanisms of change in psychotherapy research. Annu Rev Clin Psychol. 2007;3:1–27.
Kraemer HC, Kazdin AE, Offord DR, Kessler RC, Jensen PS, Kupfer DJ. Coming to terms with the terms of risk. Arch Gen Psychiatry. 1997;54(4):337–43.
Tabak RG, Khoong EC, Chambers DA, Brownson RC. Bridging research and practice: models for dissemination and implementation research. Am J Prev Med. 2012;43(3):337–50.
Breimaier HE, Heckemann B, Halfens RJ, Lohrmann C. The consolidated framework for implementation research (CFIR): a useful theoretical framework for guiding and evaluating a guideline implementation process in a hospital-based nursing practice. BMC Nurs. 2015;14 https://doi.org/10.1186/s12912-015-0088-4.
Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. Making psychological theory useful for implementing evidence based practice: a consensus approach. BMJ Qual Saf. 2005;14(1):26–33.
Rabin BA, Lewis CC, Norton WE, Neta G, Chambers D, Tobin JN, et al. Measurement resources for dissemination and implementation research in health. Implement Sci. 2015;11 https://doi.org/10.1186/s13012-016-0401-y.
Chaudoir SR, Dugan AG, Barr CH. Measuring factors affecting implementation of health innovations: a systematic review of structural, organizational, provider, patient, and innovation level measures. Implement Sci. 2013;8 https://doi.org/10.1186/1748-5908-8-22.
Beidas RS, Aarons GA, Barg F, Evans AC, Hadley T, Hoagwood KE, et al. Policy to implementation: evidence-based practice in community mental health–study protocol. Implement Sci. 2013;8 https://doi.org/10.1186/1748-5908-8-38.
Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4 https://doi.org/10.1186/1748-5908-4-50.
Aarons GA, Hurlburt M, Horwitz SM. Advancing a conceptual model of evidence-based practice implementation in public service sectors. Admin Pol Ment Health. 2011;38(1):4–23.
Stetler CB, Ritchie JA, Rycroft-Malone J, Charns MP. Leadership for evidence-based practice: strategic and functional behaviors for institutionalizing EBP. Worldviews Evid-Based Nurs. 2014;11(4):219–26.
Flottorp SA, Oxman AD, Krause J, Musila NR, Wensing M, Godycki-Cwirko M, et al. A checklist for identifying determinants of practice: a systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice. Implement Sci. 2013;8 https://doi.org/10.1186/1748-5908-8-35.
Birken SA, Lee S-YD, Weiner BJ. Uncovering middle managers' role in healthcare innovation implementation. Implement Sci. 2012;7 https://doi.org/10.1186/1748-5908-7-28.
Guerrero EG, Frimpong J, Kong Y, Fenwick K, Aarons GA. Advancing theory on the multilevel role of leadership in the implementation of evidence-based health care practices. Health Care Manag Rev. 2018; https://doi.org/10.1097/HMR.0000000000000213.
Aarons GA, Wells RS, Zagursky K, Fettes DL, Palinkas LA. Implementing evidence-based practice in community mental health agencies: a multiple stakeholder analysis. Am J Public Health. 2009;99(11):2087–95.
Aarons GA, Ehrhart MG, Farahnak LR, Sklar M. Aligning leadership across systems and organizations to develop a strategic climate for evidence-based practice implementation. Annu Rev Public Health. 2014;35:255–74.
O'Reilly CA, Caldwell DF, Chatman JA, Lapiz M, Self W. How leadership matters: the effects of leaders' alignment on strategy implementation. Leadersh Q. 2010;21(1):104–13.
Birken S, Clary A, Tabriz AA, Turner K, Meza R, Zizzi A, et al. Middle managers’ role in implementing evidence-based practices in healthcare: a systematic review. Implement Sci. 2018;13 https://doi.org/10.1186/s13012-018-0843-5.
Aarons GA, Ehrhart MG, Farahnak LR. The implementation leadership scale (ILS): development of a brief measure of unit level implementation leadership. Implement Sci. 2014;9 https://doi.org/10.1186/1748-5908-9-45.
Kozlowski SW, Doherty ML. Integration of climate and leadership: examination of a neglected issue. J Appl Psychol. 1989;74(4):546–53.
Birken SA, Lee S-YD, Weiner BJ, Chin MH, Schaefer CT. Improving the effectiveness of health care innovation implementation: middle managers as change agents. Med Care Res Rev. 2013;70(1):29–45.
Aarons GA, Ehrhart MG, Moullin JC, Torres EM, Green AE. Testing the leadership and organizational change for implementation (LOCI) intervention in substance abuse treatment: a cluster randomized trial study protocol. Implement Sci. 2017;12 https://doi.org/10.1186/s13012-017-0562-3.
Ehrhart MG, Aarons GA, Farahnak LR. Assessing the organizational context for EBP implementation: the development and validity testing of the implementation climate scale (ICS). Implement Sci. 2014;9 https://doi.org/10.1186/s13012-014-0157-1.
Weiner BJ, Belden CM, Bergmire DM, Johnston M. The meaning and measurement of implementation climate. Implement Sci. 2011;6 https://doi.org/10.1186/1748-5908-6-78.
Klein KJ, Sorra JS. The challenge of innovation implementation. Acad Manag Rev. 1996;21(4):1055–80.
Jacobs SR, Weiner BJ, Bunger AC. Context matters: measuring implementation climate among individuals and groups. Implement Sci. 2014;9 https://doi.org/10.1186/1748-5908-9-46.
Schneider B, Ehrhart MG, Macey WH. Organizational climate and culture. Annu Rev Psychol. 2013;64:361–88.
Hong Y, Liao H, Hu J, Jiang K. Missing link in the service profit chain: a meta-analytic review of the antecedents, consequences, and moderators of service climate. J Appl Psychol. 2013;98(2):237–67.
Glisson C. The organizational context of children's mental health services. Clin Child Fam Psychol Rev. 2002;5(4):233–53.
Kuenzi M, Schminke M. Assembling fragments into a lens: a review, critique, and proposed research agenda for the organizational work climate literature. J Manage. 2009;35(3):634–717.
Powell BJ, Mandell DS, Hadley TR, Rubin RM, Evans AC, Hurford MO, et al. Are general and strategic measures of organizational context and leadership associated with knowledge and attitudes toward evidence-based practices in public behavioral health settings? A cross-sectional observational study. Implement Sci. 2017;12 https://doi.org/10.1186/s13012-017-0593-9.
Shuman CJ, Liu X, Aebersold ML, Tschannen D, Banaszak-Holl J, Titler MG. Associations among unit leadership and unit climates for implementation in acute care: a cross-sectional study. Implement Sci. 2018;13 https://doi.org/10.1186/s13012-018-0753-6.
Williams NJ, Ehrhart MG, Aarons GA, Marcus SC, Beidas RS. Linking molar organizational climate and strategic implementation climate to clinicians’ use of evidence-based psychotherapy techniques: cross-sectional and lagged analyses from a 2-year observational study. Implement Sci. 2018;13 https://doi.org/10.1186/s13012-018-0781-2.
Guerrero EG, Fenwick K, Kong Y. Advancing theory development: exploring the leadership–climate relationship as a mechanism of the implementation of cultural competence. Implement Sci. 2017;12 https://doi.org/10.1186/s13012-017-0666-9.
Bass BM, Riggio RE. Transformational leadership. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.; 2006.
Klein KJ, Conn AB, Sorra JS. Implementing computerized technology: an organizational analysis. J Appl Psychol. 2001;86(5):811–24.
Schein EH. Organizational culture and leadership. 4th ed. San Francisco, CA: Wiley; 2010.
Lyon AR, Cook CR, Brown EC, Locke J, Davis C, Ehrhart M, et al. Assessing organizational implementation context in the education sector: confirmatory factor analysis of measures of implementation leadership, climate, and citizenship. Implement Sci. 2018;13 https://doi.org/10.1186/s13012-017-0705-6.
Torres EM, Ehrhart MG, Beidas RS, Farahnak LR, Finn NK, Aarons GA. Validation of the implementation leadership scale (ILS) with supervisors' self-ratings. Community Ment Health J. 2017;54(1):49–53.
Beidas RS, Williams NJ, Becker-Haimes E, Aarons G, Barg F, Evans A, et al. A repeated cross-sectional study of clinicians’ use of psychotherapy techniques during 5 years of a system-wide effort to implement evidence-based practices in Philadelphia. Implement Sci. 2019;14 https://doi.org/10.1186/s13012-019-0912-4.
Wing C, Simon K, Bello-Gomez RA. Designing difference in difference studies: best practices for public health policy research. Annu Rev Public Health. 2018;39:453–69.
Lechner M. The estimation of causal effects by difference-in-difference methods. Foundations Trends® Econometrics. 2011;4(3):165–224.
Allison PD. Fixed effects regression models. Thousand Oaks, CA: SAGE Publications; 2009.
von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7.
Beidas RS, Marcus S, Aarons GA, Hoagwood KE, Schoenwald S, Evans AC, et al. Predictors of community therapists’ use of therapy techniques in a large public mental health system. JAMA Pediatr. 2015;169(4):374–82.
Beidas RS, Stewart RE, Adams DR, Fernandez T, Lustbader S, Powell BJ, et al. A multi-level examination of stakeholder perspectives of implementation of evidence-based practices in a large urban publicly-funded mental health system. Admin Pol Ment Health. 2016;43(6):893–908.
Powell BJ, Beidas RS, Rubin RM, Stewart RE, Wolk CB, Matlin SL, et al. Applying the policy ecology framework to Philadelphia’s behavioral health transformation efforts. Admin Pol Ment Health. 2016;43(6):909–26.
Stirman SW, Buchhofer R, McLaulin JB, Evans AC, Beck AT. Public-academic partnerships: the Beck initiative: a partnership to implement cognitive therapy in a community behavioral health system. Psychiatr Serv. 2009;60(10):1302–4.
Foa EB, Hembree EA, Cahill SP, Rauch SA, Riggs DS, Feeny NC, et al. Randomized trial of prolonged exposure for posttraumatic stress disorder with and without cognitive restructuring: outcome at academic and community clinics. J Consult Clin Psychol. 2005;73(5):953–64.
Cohen JA, Deblinger E, Mannarino AP, Steer RA. A multisite, randomized controlled trial for children with sexual abuse–related PTSD symptoms. J Am Acad Child Adolesc Psychiatry. 2004;43(4):393–402.
Linehan MM, Comtois KA, Murray AM, Brown MZ, Gallop RJ, Heard HL, et al. Two-year randomized controlled trial and follow-up of dialectical behavior therapy vs therapy by experts for suicidal behaviors and borderline personality disorder. Arch Gen Psychiatry. 2006;63(7):757–66.
Thomas R, Abell B, Webb HJ, Avdagic E, Zimmer-Gembeck MJ. Parent-child interaction therapy: a meta-analysis. Pediatrics. 2017;140(3):e20170352.
Weersing VR, Weisz JR, Donenberg GR. Development of the therapy procedures checklist: a therapist-report measure of technique use in child and adolescent treatment. J Clin Child Adolesc Psychol. 2002;31(2):168–80.
Kolko DJ, Cohen JA, Mannarino AP, Baumann BL, Knudsen K. Community treatment of child sexual abuse: a survey of practitioners in the national child traumatic stress network. Admin Pol Ment Health. 2009;36(1):37–49.
Weisz JR, Kuppens S, Eckshtain D, Ugueto AM, Hawley KM, Jensen-Doss A. Performance of evidence-based youth psychotherapies compared with usual clinical care: a multilevel meta-analysis. JAMA Psychiatry. 2013;70(7):750–61.
Klein JB, Jacobs RH, Reinecke MA. Cognitive-behavioral therapy for adolescent depression: a meta-analytic investigation of changes in effect-size estimates. J Am Acad Child Adolesc Psychiatry. 2007;46(11):1403–13.
Reynolds S, Wilson C, Austin J, Hooper L. Effects of psychotherapy for anxiety in children and adolescents: a meta-analytic review. Clin Psychol Rev. 2012;32(4):251–62.
McCart MR, Sheidow AJ. Evidence-based psychosocial treatments for adolescents with disruptive behavior. J Clin Child Adolesc Psychol. 2016;45(5):529–63.
Chorpita BF, Daleiden EL, Ebesutani C, Young J, Becker KD, Nakamura BJ, et al. Evidence-based treatments for children and adolescents: an updated review of indicators of efficacy and effectiveness. Clin Psychol (New York). 2011;18(2):154–72.
Abbass AA, Rabung S, Leichsenring F, Refseth JS, Midgley N. Psychodynamic psychotherapy for children and adolescents: a meta-analysis of short-term psychodynamic models. J Am Acad Child Adolesc Psychiatry. 2013;52(8):863–75.
De Nadai AS, Storch EA. Design considerations related to short-term psychodynamic psychotherapy. J Am Acad Child Adolesc Psychiatry. 2013;52(11):1213–4.
Chan D. Functional relations among constructs in the same content domain at different levels of analysis: a typology of composition models. J Appl Psychol. 1998;83(2):234–46.
Ehrhart MG, Raver JL. The effects of organizational climate and culture on productive and counterproductive behavior. In: Schneider B, Barbera K, editors. The Oxford handbook of organizational climate and culture. New York: Oxford University Press; 2014. p. 153–76.
James LR, Choi CC, Ko CE, McNeil PK, Minton MK, Wright MA, et al. Organizational and psychological climate: a review of theory and research. Eur J Work Organ Psychol. 2008;17(1):5–32.
Glisson C, Landsverk J, Schoenwald SK, Kelleher K, Hoagwood KE, Mayberg S, et al. Assessing the organizational social context (OSC) of mental health services: implications for research and practice. Admin Pol Ment Health. 2008;35:98–113.
Olin SS, Williams NJ, Pollock M, Armusewicz K, Kutash K, Glisson C, et al. Quality indicators for family support services and their relationship to organizational social context. Admin Pol Ment Health. 2014;41(1):43–54.
Glisson C, Green P, Williams NJ. Assessing the organizational social context (OSC) of child welfare systems: implications for research and practice. Child Abuse Negl. 2012;36(9):621–32.
Aarons GA, Glisson C, Green PD, Hoagwood KE, Kelleher KJ, Landsverk JA. The organizational social context of mental health services and clinician attitudes toward evidence-based practice: a United States national study. Implement Sci. 2012;7 https://doi.org/10.1186/1748-5908-7-56.
Aarons GA. Transformational and transactional leadership: association with attitudes toward evidence-based practice. Psychiatr Serv. 2006;57(8):1162–9.
Aarons GA, Sommerfeld DH. Leadership, innovation climate, and attitudes toward evidence-based practice during a statewide implementation. J Am Acad Child Adolesc Psychiatry. 2012;51(4):423–31.
Brimhall KC, Fenwick K, Farahnak LR, Hurlburt MS, Roesch SC, Aarons GA. Leadership, organizational climate, and perceived burden of evidence-based practice in mental health services. Admin Pol Ment Health. 2016;43(5):629–39.
Aarons GA, Green AE, Trott E, Willging CE, Torres EM, Ehrhart MG, et al. The roles of system and organizational leadership in system-wide evidence-based intervention sustainment: a mixed-method study. Admin Pol Ment Health. 2016;43(6):991–1008.
Bass BM, Avolio BJ. Multifactor leadership questionnaire. Mind Garden: Redwood City, CA; 2000.
Avolio BJ. Full range leadership development. 2nd ed. Thousand Oaks, CA: SAGE Publications; 2011.
Antonakis J, Avolio BJ, Sivasubramaniam N. Context and leadership: an examination of the nine-factor full-range leadership theory using the multifactor leadership questionnaire. Leadersh Q. 2003;14(3):261–95.
Aarons GA. Mental health provider attitudes toward adoption of evidence-based practice: The Evidence-Based Practice Attitude Scale (EBPAS). Mental health services research. 2004;6(2):61–74.
Klein KJ, Kozlowski SW. Multilevel theory, research, and methods in organizations: foundations, extensions, and new directions. San Francisco, CA: Jossey-Bass; 2000.
Brown RD, Hauenstein NM. Interrater agreement reconsidered: an alternative to the rwg indices. Organ Res Methods. 2005;8(2):165–84.
LeBreton JM, Senter JL. Answers to 20 questions about interrater reliability and interrater agreement. Organ Res Methods. 2008;11(4):815–52.
Croissant Y, Millo G. Panel data econometrics in R: the plm package. J Stat Softw. 2008;27(2):1–43.
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
Hedeker D, Gibbons RD. Longitudinal data analysis. Hoboken, NJ: Wiley; 2006.
MacKinnon DP. Introduction to statistical mediation analysis. New York: Taylor & Francis Group; 2008.
Preacher KJ. Advances in mediation analysis: a survey and synthesis of new developments. Annu Rev Psychol. 2015;66:825–52.
Preacher KJ, Selig JP. Advantages of Monte Carlo confidence intervals for indirect effects. Commun Methods Meas. 2012;6(2):77–98.
Hayes AF, Scharkow M. The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: does method really matter? Psychol Sci. 2013;24(10):1918–27.
Hayes AF. Beyond baron and Kenny: statistical mediation analysis in the new millennium. Commun Monogr. 2009;76(4):408–20.
MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593–614.
Powell BJ, Fernandez ME, Williams NJ, Aarons GA, Beidas RS, Lewis CC, et al. Enhancing the impact of implementation strategies in healthcare: a research agenda. Front Public Health. 2019;7 https://doi.org/10.1097/HCO.0000000000000067.
Aarons GA, Ehrhart MG, Farahnak LR, Hurlburt MS. Leadership and organizational change for implementation (LOCI): a randomized mixed method pilot study of a leadership and organization development intervention for evidence-based practice implementation. Implement Sci. 2015;10(1) https://doi.org/10.1186/s13012-014-0192-y.
Soo S, Berta W, Baker GR. Role of champions in the implementation of patient safety practice change. Healthc Q. 2009;12:123–8.
Proctor E, Ramsey AT, Brown MT, Malone S, Hooley C, McKay V. Training in implementation practice leadership (TRIPLE): evaluation of a novel practice change strategy in behavioral health organizations. Implement Sci. 2019;14 https://doi.org/10.1186/s13012-019-0906-2.
Egeland KM, Skar A-MS, Endsjø M, Laukvik EH, Bækkelund H, Babaii A, et al. Testing the leadership and organizational change for implementation (LOCI) intervention in Norwegian mental health clinics: a stepped-wedge cluster randomized design study protocol. Implement Sci. 2019;14 https://doi.org/10.1186/s13012-019-0873-7.
Brookman-Frazee L, Stahmer AC. Effectiveness of a multi-level implementation strategy for ASD interventions: study protocol for two linked cluster randomized trials. Implement Sci. 2018;13 https://doi.org/10.1186/s13012-018-0757-2.
Hogue A, Dauber S, Lichvar E, Bobek M, Henderson CE. Validity of therapist self-report ratings of fidelity to evidence-based practices for adolescent behavior problems: correspondence between therapists and observers. Admin Pol Ment Health. 2015;42(2):229–43.
We are grateful for the support that the Department of Behavioral Health and Intellectual disAbility Services have provided to facilitate this work within Philadelphia, for the Evidence Based Practice and Innovation (EPIC) group, and for the partnership provided to us by participating agencies, leadership, therapists, youth, and families.
This research was supported by grants from the U.S. National Institute of Mental Health: K23 MH099179 (PI: Beidas) and P50MH113840 (PIs: Beidas, Mandell, Volpp). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Ethics approval and consent to participate
The institutional review board (IRB) at the City of Philadelphia approved this study on October 2nd, 2012 (Protocol Number 2012–-41). The City of Philadelphia IRB serves as the IRB of record. The University of Pennsylvania also approved all study procedures on September 14th, 2012 (Protocol Number 816619). Written informed consent and/or assent was obtained for all study participants.
Consent for publication
RB receives royalties from Oxford University Press and has consulted for Merck and the Camden Coalition of Health Providers. The other authors (NJW, CBW, EBH) declare that they have no competing interests.
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Williams, N.J., Wolk, C.B., Becker-Haimes, E.M. et al. Testing a theory of strategic implementation leadership, implementation climate, and clinicians’ use of evidence-based practice: a 5-year panel analysis. Implementation Sci 15, 10 (2020). https://doi.org/10.1186/s13012-020-0970-7
- Implementation leadership
- Implementation climate
- Behavioral health
- Evidence-based practice