Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Jul 13.
Published in final edited form as: Adm Policy Ment Health. 2019 Jan;46(1):71–81. doi: 10.1007/s10488-018-0896-4

Evidence-Based Treatment Strategies in Youth Mental Health Services: Results from a National Survey of Providers

Evelyn Cho 1, Phillip K Wood 1, Erin K Taylor 1, Estee M Hausman 1, Jack H Andrews 1, Kristin M Hawley 1
PMCID: PMC7357714  NIHMSID: NIHMS1604057  PMID: 30209703

Abstract

Previous surveys indicate infrequent use of evidence-based treatment (EBT) manuals in usual care youth mental health, but the extent to which providers use core and common EBT strategies and what contextual factors impact EBT strategy implementation need further study. In a national, multidisciplinary survey of 1,092 youth-serving providers, providers reported regular use of many EBT strategies. Provider learning theory orientation, more recent degree, more standardized and ongoing assessment use, more positive attitudes toward innovation and evidence, fewer low-income clients, and perceptions that their agency valued quality care and provided fewer training resources predicted more frequent EBT strategy use.

Keywords: usual care, evidence-based treatment, youth mental health, dissemination, implementation

Numerous randomized clinical trials demonstrate beneficial outcomes of evidence-based treatments (EBTs) for youth mental health (MH) problems. EBTs for the most common youth MH problems (i.e., anxiety, depression, disruptive behavior) consistently outperform control conditions (e.g., Lonigan, Elbert, & Johnson, 1998; Silverman & Hinshaw, 2008) and usual care (UC; Weisz et al., 2013; Weisz, Jensen-Doss, & Hawley, 2006). Since the mid-1990s, there have been widespread calls for the integration of EBTs into UC (NAMHC, 2001) along with publicly- and privately-funded initiatives to improve EBT training and implementation (e.g., EBT mandates in publicly-funded services; Cooper & Aratani, 2009). Despite such efforts, the extent to which providers actually implement EBTs in UC youth MH care is not entirely clear.

Most research on EBT use comes from MH provider surveys. Addis and Krasnow (2000) surveyed 669 psychologists and found 7% used manuals often or almost exclusively, 24% sometimes, 22% rarely, and 47% never. Becker, Smith, and Jensen-Doss (2013) surveyed 734 counselors, marriage and family therapists (MFTs), and social workers, and found a similar pattern: 8% used manuals frequently, 51% to some degree, and 41% never. Walrath, Sheehan, Holden, Hernandez, and Blau (2006) surveyed 467 providers who reported using EBTs in the Comprehensive Community Mental Health Services for Children and their Families Program (a federal program that funds a coordinated system of care for youth); the majority reported approaches consistent with EBTs (e.g., 62% used CBT), but use of specific manuals was rare (e.g., 8% used Parent-Child Interaction Therapy) and even fewer implemented the full manual.

Given both provider reticence to use lockstep manuals and substantial overlap in the strategies prescribed across different manuals for a given target problem and age group, experts have suggested broadening implementation to core treatment strategies common across EBTs rather than just specific, branded manuals (Chorpita, Daleiden, & Weisz, 2005; Garland, Hawley, Brookman-Frazee, & Hurlburt, 2008). In one of the only observational studies of UC youth MH services, Garland et al. (2010) coded use of common EBT strategies for behavior problems for 191 youth (4–13 years old) treated by 96 providers in publicly funded agencies in San Diego County. They found that providers used the full range of EBT strategies for behavior problems but implemented some far more often than others (e.g., therapist reinforcement in 83% vs. role-playing new skills in just 35% of sessions) and all observed strategies were delivered at low intensity. Among 616 providers contracted with Hawaii’s Child and Adolescent Mental Health Division, Higa-McMillan, Kotte, Jackson, and Daleiden (2017) found providers reported using the full range of EBT strategies for youth anxiety (3–19 years old) though again there was variability between strategies (e.g., 93% used problem solving vs. 15% used exposure). Finally, as part of a citywide CBT training initiative, Beidas et al. (2015) surveyed 130 providers from the largest youth-serving agencies in Philadelphia; providers reported using CBT strategies “sometimes” to “often.” Existing studies focus on settings with EBT implementation projects underway (e.g., Beidas et al., 2015; Higa-McMillan et al., 2017), or a single problem or narrow age range (e.g., behavior disorders in youth 4–13 years old in Garland et al., 2010). Building on this important work, a large national sample of UC youth MH providers may help to clarify whether, and to what extent, providers implement EBT strategies in UC youth MH settings.

The design and execution of future EBT implementation initiatives would also benefit from improved understanding of factors that influence EBT use. Several implementation frameworks suggest structural, organizational, provider, client, and innovation factors impact EBT use (e.g., Chaudoir, Dugan, & Barr, 2013; Damschroder et al., 2009; Glisson, 2002; Greenhalgh, Bate, Kyriakidou, Macfarlane, & Robert, 2004; Rogers, 2010; Schoenwald & Hoagwood, 2001). Provider characteristics have received perhaps the most attention to date. Positive attitudes toward research (Nelson & Steele, 2007), learning theory orientation (Becker et al., 2013; Nelson & Steele, 2007), less clinical experience (Brookman-Frazee, Haine, Baker-Ericzén, Zoffness, & Garland, 2010), and being a psychologist (Higa-McMillan, Nakamura, Morris, Jackson, & Slavin, 2015) have been associated with more frequent EBT use. Being trained in EBTs also predicted EBT use in several (Nelson & Steele, 2007; Wolk et al., 2016), though not all (Jensen-Doss, Cusack, & de Arellano, 2008) training studies.

Contextual factors suggested by implementation frameworks have also shown promise. Glisson et al. (2008) found that providers in youth-serving MH organizations characterized by low rigidity and resistance to change, and high expectations for their providers to have up-to-date knowledge and to prioritize client well-being, sustained use of a new treatment twice as long as organizations with higher rigidity and resistance, and lower expectations. Providers may also use EBT strategies differentially based on youth characteristics. Providers have been found to use EBTs more often for externalizing problems (Wolk et al., 2016), and with older youth and better educated, higher income families (Brookman-Frazee et al., 2010; Higa-McMillan et al., 2015). Given concerns that EBTs do not address the needs of multi-problem youth (Nelson, Steele, & Mize, 2006), fewer co-occurring problems may also predict more EBT use.

The current study aimed to replicate and extend previous research on UC youth MH providers’ EBT strategy use within a large, national sample of UC providers, which may help focus future training and implementation efforts. We conducted a multidisciplinary, national survey of providers to (1) determine whether, and to what extent, providers report using EBT strategies for the most common youth MH referral concerns (i.e., anxiety, depression, disruptive behavior), and (2) identify provider and contextual factors that predict EBT strategy use. Guided by prior work, we hypothesized that more positive attitudes toward innovation and evidence, more frequent standardized and ongoing assessment use, psychology discipline, learning theory orientation, fewer years since degree, greater provider perceptions that their agencies value quality care and support ongoing training, fewer low-income cases, primary behavior problem, and fewer co-occurring problems would predict greater EBT strategy use. We had no a priori hypotheses for case mix racial and ethnic minority status and age.

Method

Participants

Participants were 1,092 MH providers (33.15% psychologists, 24.27% counselors, 22.16% MFTs, 21.98% social workers, and 16.12% psychiatrists) who reported providing treatment to youth. Participants had a mean age of 52.58 (SD = 9.81), were majority female (62.64%); and White (92.22%; 2.01% African American, 1.92% Asian or Pacific Islander, 0.64% Native American, 0.92% other; 2.9% were Hispanic or Latino/a). Participants were 53.57% doctoral-and 46.43% master’s-level providers; had a mean of 20.19 years since their highest degree; had a mean of 39.71 active cases; and 39.93% reported a learning theory orientation. Participants worked in a range of settings: 63.55% private practice, 18.86% outpatient clinic, 9.25%university or medical school, 9.07% school settings, 6.32% residential or day treatment facility, 5.40% inpatient hospital, and 15.48% other, with 51.74% working only in private practice. Average client case mix was reported as 31.18% racial or ethnic minority, 34.05% low income, 31.87% three to 10-year-old, and 66.67% 11 to 17-year-old youth.

Procedures

We used the Tailored Design Method for survey development (i.e., careful piloting, noncontingent incentive, multiple, personally-addressed, hand-signed mailings; Dillman, 2000). After initial development of the survey items, we conducted focus groups with 14 local mental health providers who completed the initial survey, then provided feedback on survey readability, item content, effort required for completion, overall impression of the survey, likelihood that they would complete the survey, and changes that would increase chances they would complete the survey. Based on focus group feedback, we shortened the survey and eliminated jargon. We then piloted the revised survey with a national sample of 500 providers to get additional feedback and determine the most cost-effective incentive (Hawley et al. 2009).

To administer the final survey, we randomly assigned providers to one of three versions of the survey that differed only with regards to the prompt for providers to rate how frequently they used various treatment strategies with a recent, representative youth case of primary anxiety, depression, or behavior problems. We sent up to five mailings to 5,000 providers randomly sampled from membership rosters of the largest national practice guilds for youth-serving MH providers (1,000 each): American Counseling Association (ACA), American Association for Marriage and Family Therapy (AAMFT), American Academy of Child and Adolescent Psychiatry (AACAP), American Psychological Association (APA), and National Association of Social Workers (NASW). The first mailing, sent to all providers, included a personally addressed, hand-signed pre-notice letter. The second mailing, sent to all providers, included a personally addressed, hand-signed cover letter, $2 bill, survey, and pre-addressed, stamped return envelope. The third mailing, sent to all providers, was a personally addressed, hand-signed postcard thanking providers for returning or reminding them to return the survey. The fourth mailing, sent to non-responders only, included a second personalized cover letter, survey, and stamped return envelope. The fifth mailing, sent to nonresponders only, included a third personalized cover letter, survey, and business reply return envelope. Some 347 (6.94%) were undeliverable. Of the 4,653 presumably delivered, 2,863 (61.53%) were completed, 151 (3.25%) declined to participate, and 1,639 (35.22%) did not respond. Of respondents, 1,233 reported providing treatment to youth. The unadjusted response rate was 36.60% for AACAP, 56.70% for APA, 60.90% for AAMFT, 62.50% for ACA, and 69.60% for NASW. Response rates differed by guild, χ2 (4, N = 5,000) = 253.40, p < .001. Psychiatrists responded at a lower rate and social workers at a higher rate than others (Bonferroni corrected p value of .005). Compared to prior studies of youth MH providers (Garland, Kruse, & Aarons, 2003; Hawley & Weisz, 2003; Jensen-Doss, Hawley, Lopez, & Osterberg, 2009), our sample may somewhat underrepresent female and racial and ethnic minority providers, and over-represent doctoral-level providers. All procedures were approved by the university’s Institutional Review Board. Results from the larger sample focusing on providers’ assessment practices have been reported in prior publications (Cook et al. 2017; Jensen-Doss and Hawley 2010, 2011; Kearns and Hawley 2014).

Measures

Provider Characteristics.

We adapted provider characteristic items from past MH provider surveys (Kazdin, Siegel, & Bass, 1990). Providers reported sex, age, discipline, theoretical orientation, and year of highest degree.

Provider Attitudes Toward Innovation and Evidence (PATIE).

We created a latent variable measuring providers’ attitudes toward innovative treatment approaches and research evidence. We adapted the three highest loading items on the Openness scale of the Evidence-Based Practice Attitude Scale (Aarons, 2004). Items were rated from one (strongly disagree) to five (strongly agree); higher scores reflect more positive attitudes (α = .64, λ = .51 – .68).

Standardized Measure Use (SMU).

We created a latent variable measuring providers’ use of standardized measures using five indicators generated from a review of provider attitudes toward evidence-based assessment (Garland et al., 2003; Gilbody, House, & Sheldon, 2002) and recommended evidence-based assessment approaches for common youth mental health problems (Mash & Hunsley, 2005). Five items measured frequency of structured diagnostic interview, formal mental status exam, formal observational coding system, standardized symptoms or functioning checklists, and formal clinician symptoms or functioning ratings use. Use was rated from one (never or almost never) to five (all or most of the time) (α = .70, λ = .40 – .66).

Ongoing Assessment.

Providers reported how frequently they conducted ongoing assessment from one (never or almost never) to five (all or most of the time).

Contextual Characteristics.

Providers reported the percent of their cases that were low-income and racial or ethnic minority. Youth primary problem was determined by survey version (i.e., anxiety, depression, behavior). Providers reported youth age (i.e., 3–6, 7–10, 11–13, 14–17 years) and all co-occurring problems (i.e., anxiety, disruptive behavior, depression, Attention-Deficit Hyperactivity Disorder, learning disorder, mental retardation/developmental disorder, eating disorder, substance use/abuse, or history of abuse or trauma).

Provider Perceptions of Agency Value of Quality Care (PPAVQC).

We created a latent variable measuring individual providers’ perceptions of how much their agency values quality care using six items adapted from the Children’s Services Survey (Glisson & James, 2002). Items were rated from one (strongly disagree) to five (strongly agree); higher scores reflect higher perceived agency value of quality care (α = .82, λ = .51 – .77).

Provider Perceptions of Agency Training Resources (PPATR).

We created a latent variable measuring individual providers’ perceptions of their agency’s training resources from four items adapted from the Children’s Services Survey (Glisson & James, 2002). Items were rated from one (strongly disagree) to five (strongly agree); higher scores reflect higher perceived availability of resources (α = .77, λ = .50 – .85). Error terms for several PPAVQC and PPATR indicators were correlated due to item similarity.

Evidence-Based Behavioral Strategies Scale.

While self-report measures have been developed to assess for general therapeutic approach or orientation (e.g., Therapy Procedures Checklist; TPC; Weersing, Weisz, & Donenberg, 2002) and the presence of specific strategies (Monthly Treatment Progress Summary; Borntrager, Chorpita, Orimoto, Love, & Mueller, 2015), there were no measures to assess for frequency of EBT strategy use over the course of treatment for a given youth with these MH problems. To identify EBT strategies for anxiety, depression, and disruptive behavior problems for youth 3 to 17 years old, we followed the procedures usedby Garland et al. (2008) to identify EBT strategies for 4–13-year-old youth with disruptive behavior problems. Specifically, we identified EBTs for the three problem types from empirically-supported treatment reviews (i.e., Burns, Hoagwood, & Mrazek, 1999; Carr, 2006; Chambless & Ollendick, 2001; Chambless et al., 1996; Chambless & Hollon, 1998; Franklin, Foa, Nathan, & Gorman, 1998; Lonigan et al., 1998; Roth & Fonagy, 2005; Silverman & Hinshaw, 2008; Task Force on Promotion and Dissemination of Psychological Procedures, 1995), coded the core or essential strategies within each EBT manual, retained the core strategies that appeared in multiple manuals for each problem type, reached in-house consensus, and sent this list of strategies (20 anxiety, 21 depression, 18 behavior strategies) to youth EBT experts for review. We sent the list to 51 EBT manual authors, and the 35 experts who responded (response rate adjusted for undeliverable surveys = 73%) had 88% agreement for anxiety, 91% for depression, and 98% for behavior strategies. Strategies endorsed as a core component of EBTs for the target problem by majority of experts (anxiety = 17, depression = 18, behavior = 15) were retained in our EBT strategy items. Although the Garland et al. (2008) procedure we employed differs from that of Chorpita et al. (2005) who also identified EBT strategies (they generated a list of potential strategies a priori whereas we reviewed the manuals without a pre-existing set of possible strategies), our final lists are remarkably similar. Finally, we added items from the psychodynamic scale of the TPC (Weersing, et al., 2002) to include strategies that may be widely used but lack empirical support.

On the survey, providers were presented with descriptions of 74 treatment strategies and they rated on a five-point scale (i.e., “never or almost never” to “all or most of the time”) their use of 74 strategies during treatment with a recent representative case with primary anxiety, depression, or behavior problems. Strategies were categorized as (a) common across EBTs for anxiety, depression, and behavior problems (e.g., psychoeducation); (b) specific to EBTs for a given problem type (e.g., exposure for anxiety); and (c) other strategies without research support for the problem type (e.g., gaining insight for all problems; exposure for behavior problems). Whether or not a strategy was considered EBT depended on the youth’s presentation (anxiety only; depression only; behavior problems only; anxiety and depression; anxiety and behavior; depression and behavior; and anxiety, depression, and behavior). We computed a mean score across all EBT strategies for the given presentation for each provider. Internal consistency was as follows: anxiety α = .92, depression α = .94, behavior α = .94.

Data Analysis

The three survey versions (nanx = 364, ndep = 402, nbeh = 326) were combined for analyses due to high rates of co-occurring problems (72.80% of anxiety, 91.04% of depression, and 85.58% of disruptive behavior cases had at least one co-occurring problem). The assumptions of multivariate normality and linearity were met. Based on log-likelihood, Mahalanobis distance values, and factor score scatterplots, three cases were excluded. Three bachelor’s-level providers and 138 providers who rated less than two-thirds of the treatment strategies were excluded for a final sample of 1,092 providers. Discipline was coded as psychology or other; orientation as learning theory (cognitive, behavioral, CBT) or other; and youth age as child (3–10) or adolescent (11–17 years old). Primary problem was dummy coded with behavior problems as the reference group. Co-occurring problems were the sum of all primary and co-occurring problems.

For the first aim, we present mean ratings and percentage of providers who reported any use (i.e., 2–5 rating) and high frequency use (i.e., 4–5 rating) for each EBT strategy. For the second aim, we present a multi-group analysis; we modeled EBT strategy use as a function of four latent (PPAVQC, PPATR, PATIE, SMU) and nine manifest variables (ongoing assessment, years since degree, psychology, learning theory, client age, primary problem, co-occurring problems, race/ethnicity, low-income). Multi-group analysis is a structural equation modeling approach used in situations of planned missingness. Participants with each missingness pattern are treated as separate groups and parameters are constrained to equality across these groups (Graham, Taylor, Olchowski, & Cumsille, 2006). In our sample, providers solely in private practice did not have data on agency-based latent variables and thus constituted one group, and providers working in agencies the other. As recommended (Byrne, 2012), we first tested our hypothesized model separately for each group; then developed a revised model with adequate fit in both groups. Specifically, we examined overall fit of our hypothesized model and individual regression weights separately in both groups, then revised the model by dropping predictors that were not significant in either group. The revised model was run simultaneously for both groups in a multi-group analysis with parameters for missing values in the private practice group fixed to equal those observed in the agency group; variances for all other latent variables fixed to one; factor loadings, error variances, indicator intercepts, correlations between predictors, and regression coefficients for all predictors constrained to equality; and all predictor variable means freely estimated. Analyses were conducted in MPlus 7.0 (Muthén & Muthén, 2012) using maximum likelihood estimation. RMSEA of .05 or lower indicate close fit (Browne & Cudeck, 1993), and CFI and TLI of .90 or higher indicate adequate fit (Hu & Bentler, 1998).

Results

Provider Use of EBT Strategies in UC Settings

Mean EBT strategy ratings corresponded to the “sometimes” and “often” response options (anxiety = 3.20–4.87; depression = 3.07–4.84; behavior = 2.98–4.71). For anxiety, any use ranged from 87.64 to 100%; high frequency use from 35.44 to 99.73%. For depression, any use ranged from 81.59 to 100%; high frequency use from 30.10 to 100%. For disruptive behavior, any use ranged from 82.52 to 100%; high frequency use from 23.93 to 97.55% (see Tables 1–3 for descriptive statistics on EBT strategy use by target problem).

Table 1.

Summary of Provider Use of EBT Strategies for Youth with Primary Anxiety Disorders

EBT Strategy Any Usea High Frequency Useb M SD
Rapport / Working Alliance 100.00% 99.73% 4.87 0.34
Psychoeducation 99.45% 93.96% 4.63 0.66
Treatment Goals Discussion / Consensus 99.18% 85.16% 4.33 0.77
Affective Education 98.35% 82.97% 4.25 0.92
Cognitive Model 98.35% 87.64% 4.35 0.85
Future Planning 98.08% 67.86% 3.98 0.82
Praise or Reward of Skills / Behaviors 98.08% 67.31% 4.04 0.83
Treatment Description / Rationale 97.80% 75.00% 4.08 0.97
Cognitive Restructuring 96.43% 53.02% 3.69 0.91
Problem Solving Skills 96.43% 59.07% 3.85 0.98
Positive Self-Talk 95.60% 79.67% 4.07 1.05
Self-Monitoring and Reward 94.51% 58.79% 3.62 1.10
Provider Modeling of Skills / Behaviors 93.41% 60.44% 3.68 1.07
Graduated Exposure 93.41% 65.11% 3.71 1.17
Perspective Taking 92.86% 54.95% 3.48 1.13
In-Session Practice / Role Play 92.31% 43.13% 3.46 1.04
Relaxation Training 88.19% 35.44% 3.20 1.11
Parent Training - Reinforcement 88.19% 60.44% 3.54 1.28
Out-of-Session Practice / Homework 87.64% 55.49% 3.43 1.27
Open in a new tab

Note.

a

Any use indicates mean ratings of 2 to 5.

b

High frequency use indicates mean ratings of 4 to 5.

Table 3.

Summary of Provider Use of EBT Strategies for Youth with Primary Disruptive Behavior Disorders

EBT Strategy Any Usea High Frequency Useb M SD
Rapport / Working Alliance 100.00% 97.55% 4.71 0.51
Praise or Reward of Skills / Behaviors 99.69% 77.30% 4.23 0.69
Psychoeducation 99.39% 92.64% 4.52 0.66
Treatment Description / Rationale 99.08% 76.99% 4.04 0.87
Future Planning 98.47% 67.79% 3.97 0.76
Treatment Goals Discussion / Consensus 98.47% 83.44% 4.28 0.73
Problem Solving Skills 98.16% 71.17% 4.04 0.87
Cognitive Model 98.16% 82.52% 4.22 0.93
Affective Education 97.85% 75.77% 4.07 0.94
Specific Agenda / Session Plan 97.55% 69.63% 3.91 0.95
Parent Training - Reinforcement 96.63% 75.77% 4.01 0.99
Parent Training - Parent-Child Relationship 96.01% 64.72% 3.96 0.92
Parent Training - Commands 96.01% 74.54% 3.96 1.02
Parent Training - Consequences 96.01% 78.83% 4.08 1.00
Cognitive Restructuring 95.09% 53.68% 3.67 0.97
Self-Monitoring and Rewards 95.09% 59.20% 3.62 1.07
Parent Training - Selective Ignoring 95.09% 67.18% 3.75 1.06
Provider Modeling of Skills / Behaviors 95.09% 68.10% 3.87 0.99
Parent Training - Monitoring 93.56% 62.88% 3.68 1.12
In-Session Practice / Role Play 92.64% 47.85% 3.52 1.01
Perspective Taking 92.64% 53.68% 3.44 1.07
Parent Personal Coping 92.64% 59.20% 3.55 1.15
Out-of-Session Practice / Homework 89.57% 49.69% 3.34 1.22
Relaxation Training 88.34% 23.93% 2.98 0.98
Parent Training - Time Out 82.52% 53.07% 3.25 1.34
Open in a new tab

Note.

a

Any use indicates mean ratings of 2 to 5.

b

High frequency use indicates mean ratings of 4 to 5.

Predictors of EBT Strategy Use

Fit indices for the hypothesized model were RMSEA = .043, CFI = .901, TLI = .874 for the agency group; and RMSEA = .048, CFI = .887, TLI = .833 for the private practice group. Primary problem, percent of racial and ethnic minority cases, co-occurring problems, youth age, and professional discipline were not significant predictors in either group and were dropped from the model. This revised model demonstrated adequate fit in a multi-group analysis (RMSEA = .045, CFI = .910, TLI = .907). Higher scores on the PPAVQC (β = .21, p = .001), PATIE (βagency = .19, p<.001; βprivate = .20, p < .001), and SMU (β = .23, p < .001); more frequent use of ongoing assessment (β = .19, p < .001); and learning theory orientation (β = .12, p < .001) predicted more frequent EBT strategy use. Higher scores on the PPATR (βagency = −.14, p = .012; βprivate = −.15, p = .013), more low-income youth (βagency = −.11, p = .001; βprivate = −.08, p = .001), and greater time since degree (β = −.10, p = .002) predicted less frequent EBT strategy use.

Discussion

Youth MH providers in this national sample endorsed the full range of EBT strategies for youth anxiety, depression, and disruptive behavior. Consistent with prior research, providers gave an average rating of “sometimes” to “often” for EBT strategy use with considerable variability across strategies. We found somewhat higher use of EBT strategies for behavior and anxiety problems than in two prior studies of this population (Garland et al., 2010; Higa-McMillan et al., 2017 respectively). As in prior research, what many consider to be the most essential components of EBTs were some of the lowest rated. Less than half of providers reported activity scheduling for depression, just under two-thirds developed a hierarchy or conducted exposures for anxiety, and half to three-quarters implemented any behavioral parenting strategy (from a low of 53% time-out to a high of 79% consequences). These findings echo prior work showing infrequent use of exposures for anxiety and behavioral parent training for disruptive behavior (Garland et al., 2010; Higa-McMillan et al., 2017), indicating a need to attend more closely to providers’ use of these key components. Our findings, alongside the growing support for transdiagnostic or core and common elements approaches (e.g., Ehrenreich, Goldstein, Wright, & Barlow, 2009; Weisz, Bearman, Santucci, & Jensen-Doss, 2017; Weisz et al., 2012), suggest that focusing on providers’ knowledge and use of key underused EBT strategies (versus lockstep manuals) may prove the most efficient use of limited training and implementation resources.

Several provider and contextual influences suggested by prior work also predicted EBT use. EBT use was associated with both standardized and ongoing assessment use. Given that these assessment practices are supported by research (e.g., Lambert et al., 2003), the positive associations were not unexpected. It also suggests that evidence-based assessment and treatment may be more connected in UC practice than thought (Youngstrom, Choukas-Bradley, Calhoun, & Jensen-Doss, 2015). Learning theory orientation, more positive attitudes toward innovation and evidence, more recent training, and fewer low-income clients predicted more EBT use. This suggests that future implementation efforts might benefit from targeting providers’ attitudes toward research-supported practices and helping providers to flexibly deliver EBTs with low-income youth who may present with barriers to EBT use (e.g., life events interfere with EBT use; Chorpita, Korathu-Larson, Knowles, & Guan, 2014). Given the relative recency of the evidence-based practice movement and its increasing emphasis in training programs (Sheehan, Walrath, & Holden, 2006), we may simply see greater saturation of EBTs within UC as new providers enter the field and others retire. Of course, if graduate school is the only time providers learn EBTs (Sheehan et al., 2006), experienced providers may be continually behind the evidence, which argues for improving the quality of continuing education trainings so they consistently impact knowledge, attitudes, and practices (Herschell, Kolko, Baumann, & Davis, 2010).

Our provider-level findings are also consistent with growing evidence that agency-based interventions may facilitate EBT implementation (e.g., Glisson et al., 2012). In our sample, the more a provider perceived their agency as valuing quality care, the more they used EBT strategies. Interestingly, providers who perceived their agency as providing more training resources actually reported less EBT use than those with fewer resources. However, these two variables (perception that agency valued quality care and perception that agency provided more training resources) were positively associated, which may indicate that agencies valuing quality care are also more likely to invest in training resources, but perhaps only when such training is needed. In other words, agencies that value quality care, but whose providers are already using EBTs, may actually invest less in training than similar agencies whose providers are not using EBTs.

In contrast to prior work (Higa-McMillan et al., 2015), psychologists did not report more EBT use than other disciplines; this may reflect widespread impact of the evidence-based practice movement across disciplines. Previous findings on youth age have been mixed (Higa-McMillan et al., 2015; Brokman-Frazee et al., 2010); in our sample, it was nonsignificant. There may be age-based differences in specific EBT strategy use that we did not examine (e.g., Wolk et al. 2016) found providers more likely to use behavioral strategies with younger and cognitive strategies with older youth). Despite provider concerns that EBTs cannot address the unique needs of UC youth (Nelson et al., 2006), co-occurring problems and proportion of racial and ethnic minority youth did not predict EBT use. This nonsignificant finding may reflect our focus on EBT strategies which may allow greater flexibility in EBT implementation than lockstep manuals (Kendall & Beidas, 2007). Given the effectiveness of common elements approaches versus lockstep manuals within UC (e.g., Weisz et al., 2012, 2017), future training and implementation efforts may wish to focus on common strategies and decision-support tools to encourage flexible fidelity to EBT (Chorpita et al., 2005).

Our design has some noteworthy limitations. Observational coding was impractical with a large national sample so we relied exclusively on provider report. Prior research suggests that providers may over-report their use of treatment strategies in a given treatment session compared to trained coders (e.g., Hogue, Dauber, Lichvar, Bobek, & Henderson, 2015; Hurlburt, Garland, Nguyen, & Brookman-Frazee, 2010). Interestingly, studies looking at a longer time span (e.g., a month) suggest that while providers still report more strategy use than trained coders, they are generally consistent with treatment experts (Chapman, McCart, Letourneau, & Sheidow, 2013) and have reached acceptable reliability with trained coders (Borntrager et al., 2015). Further, while some of these previous studies asked providers about their use of prescribed strategies (i.e., strategies providers were supposed to implement), we strove to minimize social desirability bias by asking about a wide range of strategies, none of which were labeled as “evidence-based.” Our intent was to make it unlikely that a provider would endorse a given strategy simply as the “right” answer. Still, provider self-report may account for some of the higher use of EBT we found relative to past observational studies (our sample did report greater EBT use than Garland et al. (2010)).

It should also be noted that we did not ask providers to describe their rationale for using the strategies they reported with their given case. Although we did examine the impact of several case characteristics on EBT use, we do not know how and whether those characteristics or others may have influenced clinician behavior. Evidence-based practice in psychology (American Psychological Association, 2006) includes client preference and clinical judgment, together with research support, to guide decision-making. As such, future research could query clinicians about their decision-making process in order to further our understanding of whether, and how, clinical decision-making may be informed by empirical research.

We also restricted our sample to guild members from five disciplines. Guild members may have more access to research publications and trainings through their memberships, which may have yielded a higher estimate than nonmembers. In addition, because of our decision to sample 1,000 psychiatrists, we may have somewhat over-represented doctoral providers compared to current national norms, and it should be noted that we did not use weighting procedures in our analyses to generalize our survey results to the larger provider population (Lumley, 2004). Our sample may also under-represent female and racial and ethnic minority providers, compared to previous estimates.

Our findings regarding agency features should also be interpreted with caution since we did not sample multiple providers within an agency. As such, our provider perceptions of their agency’s culture or climate are just that–one provider’s perception. To increase likelihood of response, we also adapted items from established measures in order to decrease time required to complete the survey. Finally, like previous research on attitudes toward EBTs, some PPAVQC items confounded evidence and innovation (e.g., “I like to try new types of practices that are supported by research”). Not everything new comes from science and future work could more carefully examine attitudes toward evidence separately from attitudes toward innovation.

Still, to our knowledge this is the first national, multidisciplinary survey to examine use of EBT strategies in UC. Our findings suggest that most EBT strategies are present within everyday clinical services, but key strategies (e.g., exposure; behavioral activation; parenting skills) are still implemented at low frequencies. To the extent that these strategies are in fact “key” for youth improvement, we need more focused training and implementation efforts to facilitate their consistent implementation within UC.

Table 2.

Summary of Provider Use of EBT Strategies for Youth with Primary Depressive Disorders

EBT Strategy Any Usea High Frequency Useb M SD
Rapport / Working Alliance 100.00% 100.00% 4.84 0.37
Cognitive Model 99.75% 90.30% 4.43 0.73
Future Planning 99.50% 66.67% 4.03 0.70
Treatment Goals Discussion / Consensus 99.25% 83.58% 4.26 0.82
Psychoeducation 99.00% 93.78% 4.59 0.67
Affective Education 98.76% 84.08% 4.26 0.85
Praise or Reward of Skills / Behaviors 98.51% 66.67% 4.02 0.78
Treatment Description / Rationale 98.51% 73.88% 3.98 0.93
Cognitive Restructuring 98.26% 62.44% 3.93 0.78
Positive Self-Talk 98.26% 80.10% 4.13 0.90
Problem Solving Skills 97.76% 61.44% 3.88 0.86
Social / Communication Skills 97.26% 38.31% 3.59 0.80
Specific Agenda / Session Plan 97.26% 68.16% 3.86 0.95
Provider Modeling of Skills / Behaviors 96.27% 64.68% 3.75 1.01
Pleasant Activity Scheduling 96.27% 65.42% 3.74 1.01
Self-Monitoring and Rewards 94.78% 64.68% 3.74 1.07
In-Session Practice / Role Play 91.54% 41.54% 3.40 1.03
Perspective Taking 91.54% 54.23% 3.40 1.13
Mastery Activity Scheduling 91.54% 55.47% 3.46 1.11
Relaxation Training 88.06% 30.10% 3.09 1.03
Thought-Stopping 87.81% 53.73% 3.39 1.26
Parent Training - Reinforcement 87.81% 59.70% 3.51 1.27
Out-of-Session Practice / Homework 86.82% 42.04% 3.14 1.20
Structured Activity Scheduling 81.59% 41.04% 3.07 1.27
Open in a new tab

Note.

a

Any use indicates mean ratings of 2 to 5.

b

High frequency use indicates mean ratings of 4 to 5.

Funding:

This research was supported in part by R03 MH077752 from the National Institute of Mental Health to Kristin M. Hawley.

Footnotes

Conflict of interest. The authors declare that they have no conflict of interest.

Ethical approval. All procedures performed in studies involving human participants were in accordance with the ethical standards of the University of Missouri Institutional Review Board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent. This study was approved for a waiver of signed consent. All participants were provided with a consent statement.

References

  1. Aarons GA (2004). Mental health provider attitudes toward adoption of evidence-based practise: the evidence-based practise attitude scale (EBPAS). Mental Health Services Research, 6(2), 61–74. 10.1023/b:mhsr.0000024351.12294.65 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Addis ME, & Krasnow AD (2000). A national survey of practicing psychologists’ attitudes toward psychotherapy treatment manuals. Journal of Consulting and Clinical Psychology, 68(2), 331–339. 10.1037/0022-006X.68.2.331 [DOI] [PubMed] [Google Scholar]
  3. APA Presidential Task Force on Evidence-Based Practice. (2006). Evidence-based practice in psychology. The American Psychologist, 61(4), 271 10.1037/0003-066x.61.4.271 [DOI] [PubMed] [Google Scholar]
  4. Becker EM, Smith AM, & Jensen-Doss A (2013). Who’s using treatment manuals? A national survey of practicing therapists. Behaviour Research and Therapy, 51(10), 706–710. 10.1016/j.brat.2013.07.008 [DOI] [PubMed] [Google Scholar]
  5. Beidas RS, Marcus S, Aarons GA, Hoagwood KE, Schoenwald S, Evans AC, … Mandell DS (2015). Predictors of community therapists’ use of therapy techniques in a large public mental health system. JAMA Pediatrics, 169(4), 374–382. 10.1001/jamapediatrics.2014.3736 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Borntrager CF, Chorpita BF, Orimoto T, Love A, & Mueller CW (2015). Validity of Clinician’s Self-Reported Practice Elements on the Monthly Treatment and Progress Summary. Journal of Behavioral Health Services and Research, 42(3), 367–382. 10.1007/s11414-013-9363-x [DOI] [PubMed] [Google Scholar]
  7. Brookman-Frazee L, Haine R. a., Baker-Ericzén M, Zoffness R, & Garland AF (2010). Factors Associated with Use of Evidence-Based Practice Strategies in Usual Care Youth Psychotherapy. Administration and Policy in Mental Health and Mental Health Services Research, 37(3), 254–269. 10.1007/s10488-009-0244-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Browne MW, & Cudeck R (1993). Alternative ways of assessing model fit In Bollen KA & Long JS (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks: Sage. [Google Scholar]
  9. Burns BJ, Hoagwood K, & Mrazek PJ (1999). Effective Treatment for Mental Disorders in Children and Adolescents. Clinical Child and Family Psychology Review, 2(4), 199–254. 10.1023/A:1021826216025 [DOI] [PubMed] [Google Scholar]
  10. Byrne BM (2012). Structural equation modeling with Mplus : basic concepts, applications, and programming. New York: Routledge Academic. [Google Scholar]
  11. Carr A (Ed.). (2006). Prevention: What Works with Children and Adolescents - Google Books. New York: Routledge. [Google Scholar]
  12. Chambless DL, & Hollon SD (1998). Defining Empirically Supported Therapies. Journal of Consulting and Clinical Psychology, 66(1), 7–18. Retrieved from http://www.div12.org/wp-content/uploads/2016/10/Defining-Empirically-Supported-Therapies-1.pdf [DOI] [PubMed] [Google Scholar]
  13. Chambless DL, & Ollendick TH (2001). Empirically supported psychological interventions: controversies and evidence. Annual Review of Psychology, 52(August 2015), 685–716. 10.1146/annurev.psych.52.1.685 [DOI] [PubMed] [Google Scholar]
  14. Chambless DL, Sanderson WC, Shoham V, Bennett Johnson S, Pope KS, & Crits-Christoph P (1996). An update on empirically validated therapies. The Clinical Psychologist, 49, 5–18. [Google Scholar]
  15. Chapman JE, McCart MR, Letourneau EJ, Sheidow AJ. Comparison of Youth, Caregiver, Therapist, Trained, and Treatment Expert Raters of Therapist Adherence to a Substance Abuse Treatment Protocol. Journal of consulting and clinical psychology. 2013;81(4):674–680. doi: 10.1037/a0033021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chaudoir SR, Dugan AG, & Barr CHI (2013). Measuring factors affecting implementation of health innovations: a systematic review of structural, organizational, provider, patient, and innovation level measures. Implementation Science, 8, 22 10.1186/1748-5908-8-22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chorpita BF, Daleiden EL, & Weisz JR (2005). Identifying and selecting the common elements of evidence based interventions: A distillation and matching model. Mental Health Services Research, 7(1), 5–20. 10.1007/s11020-005-1962-6 [DOI] [PubMed] [Google Scholar]
  18. Chorpita BF, Korathu-Larson P, Knowles LM, & Guan K (2014). Emergent life events and their impact on service delivery: Should we expect the unexpected? Professional Psychology: Research and Practice, 45(5), 387–393. 10.1037/a0037746 [DOI] [Google Scholar]
  19. Cook JR, Hausman EM, Jensen-Doss A, & Hawley KM (2017). Assessment practices of child clinicians. Assessment, 24(2), 210–221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Cooper JL, & Aratani Y (2009). The status of states’ policies to support evidence-based practices in children’s mental health. Psychiatric Services, 60(12), 1672–1675. 10.1176/appi.ps.60.12.1672 [DOI] [PubMed] [Google Scholar]
  21. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, & Lowery JC (2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science, 4(1), 50 10.1186/1748-5908-4-50 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dillman DA (2000). Mail and Internet Surveys: The Tailored Design Method. New York: Wiley. [Google Scholar]
  23. Ehrenreich JT, Goldstein CM, Wright LR, & Barlow DH (2009). Development of a Unified Protocol for the Treatment of Emotional Disorders in Youth. Child & Family Behavior Therapy, 31(1), 20–37. 10.1080/07317100802701228 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Franklin ME, Foa EB, Nathan PE, & Gorman JM (Eds.). (1998). A Guide to Treatments that Work. New York: Oxford. [Google Scholar]
  25. Garland AF, Hawley KM, Brookman-Frazee L, & Hurlburt MS (2008). Identifying common elements of evidence-based psychosocial treatments for children’s disruptive behavior problems. Journal of the American Academy of Child and Adolescent Psychiatry, 47(5), 505–514. 10.1097/CHI.0b013e31816765c2 [DOI] [PubMed] [Google Scholar]
  26. Garland AF, Kruse M, & Aarons GA (2003). Clinicians and Outcome Measurement: What’s the Use? Journal of Behavioral Health Services & Research, 30(4), 393–405. 10.1007/bf02287427 [DOI] [PubMed] [Google Scholar]
  27. Garland AF, Ph D, Brookman-frazee L, Hurlburt M, Accurso EC, Zoffness R, … Ganger W (2010). Mental health care for children with disruptive behavior problems : A view inside therapists’ offices. Psychiatric Services, 61(8), 788–795. 10.1176/appi.ps.61.8.788 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Gilbody SM, House AO, & Sheldon TA (2002). Psychiatrists in the UK do not use outcomes measures. British Journal of Psychiatry, 180(2), 101–103. 10.1192/bjp.180.2.101 [DOI] [PubMed] [Google Scholar]
  29. Glisson C (2002). The organizational context of children’s mental health services. Clin Child Fam Psychol Rev, 5(4), 233–253. https://doi.org/1020972906177 [DOI] [PubMed] [Google Scholar]
  30. Glisson C, Hemmelgarn A, Green P, Dukes D, Atkinson S, & Williams NJ (2012). Randomized trial of the availability, responsiveness, and continuity (arc) organizational intervention with community-based mental health programs and clinicians serving youth. Journal of the American Academy of Child and Adolescent Psychiatry, 51(8), 780–787. 10.1016/j.jaac.2012.05.010 [DOI] [PubMed] [Google Scholar]
  31. Glisson C, & James LR (2002). The cross-level effects of culture and climate in human service teams. Journal of Organizational Behavior, 23(6), 767–794. 10.1002/job.162 [DOI] [Google Scholar]
  32. Glisson C, Schoenwald SK, Kelleher K, Landsverk J, Hoagwood KE, Mayberg S, … Health, T. R. N. on Y. M. (2008). Therapist Turnover and New Program Sustainability in Mental Health Clinics as a Function of Organizational Culture, Climate, and Service Structure. Administration and Policy in Mental Health and Mental Health Services Research, 35(1–2), 124–133. 10.1007/s10488-007-0152-9 [DOI] [PubMed] [Google Scholar]
  33. Graham JW, Taylor BJ, Olchowski AE, & Cumsille PE (2006). Planned missing data designes in psychological research. Psychological Methods, 11(4), 323–343. 10.1037/1082-989X.11.4.323 [DOI] [PubMed] [Google Scholar]
  34. Greenhalgh T, Bate P, Kyriakidou O, Macfarlane F, & Robert G (2004). Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations. The Milbank Quarterly, 82(4), 607–610. 10.1111/j.0887-378x.2004.00325.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hawley KM, Cook JR, & Jensen-Doss A (2009). Do noncontingent incentives increase survey response rates among mental health providers? A randomized trial comparison. Administration and Policy in Mental Health and Mental Health Services Research, 36(5), 343–348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hawley KM, & Weisz JR (2003). Child, parent, and therapist (dis)agreement on target problems in outpatient therapy: The therapist’s dilemma and its implications. Journal of Consulting and Clinical Psychology, 71(1), 62–70. 10.1037//0022-006X.71.1.62 [DOI] [PubMed] [Google Scholar]
  37. Herschell AD, Kolko DJ, Baumann BL, & Davis AC (2010). The role of therapist training in the implementation of psychosocial treatments: A review and critique with recommendations. Clinical Psychology Review, 30(4), 448–466. 10.1016/j.cpr.2010.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Higa-McMillan CK, Nakamura BJ, Morris A, Jackson DS, & Slavin L (2015). Predictors of Use of Evidence-Based Practices for Children and Adolescents in Usual Care. Administration and Policy in Mental Health and Mental Health Services Research, 42(4), 373–383. 10.1007/s10488-014-0578-9 [DOI] [PubMed] [Google Scholar]
  39. Higa-McMillan C, Kotte A, Jackson D, & Daleiden EL (2017). Overlapping and Non-overlapping Practices in Usual and Evidence-Based Care for Youth Anxiety. Journal of Behavioral Health Services and Research, 44(4), 684–694. 10.1007/s11414-016-9502-2 [DOI] [PubMed] [Google Scholar]
  40. Hogue A, Dauber S, Lichvar E, Bobek M, & Henderson CE (2015). Validity of Therapist Self-Report Ratings of Fidelity to Evidence-Based Practices for Adolescent Behavior Problems: Correspondence between Therapists and Observers. Administration and Policy in Mental Health, 42(2), 229–243. 10.1007/s10488-014-0548-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hu L, & Bentler PM (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. 10.1037/1082-989X.3.4.424 [DOI] [Google Scholar]
  42. Hurlburt MS, Garland AF, Nguyen K, & Brookman-Frazee L (2010). Child and Family Therapy Process: Concordance of Therapist and Observational Perspectives. Administration and Policy in Mental Health, 37(3), 230–244. 10.1007/s10488-009-0251-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Jensen-Doss A, Cusack KJ, & de Arellano MA (2008). Workshop-based Training in Trauma-focused CBT: An In-depth Analysis of Impact on Provider Practices. Community Mental Health Journal, 44(4), 227–244. 10.1007/s10597-007-9121-8 [DOI] [PubMed] [Google Scholar]
  44. Jensen-Doss A, Hawley KM, Lopez M, & Osterberg LD (2009). Using evidence-based treatments: The experiences of youth providers working under a mandate. Professional Psychology: Research and Practice, 40(4), 417–424. 10.1037/a0014690 [DOI] [Google Scholar]
  45. Jensen-Doss A, & Hawley KM (2010). Understanding barriers to evidence-based assessment: Clinician attitudes toward standardized assessment tools. Journal of Clinical Child & Adolescent Psychology, 39(6), 885–896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Jensen-Doss A, & Hawley KM (2011). Understanding clinicians’ diagnostic practices: Attitudes toward the utility of diagnosis and standardized diagnostic tools. Administration and Policy in Mental Health and Mental Health Services Research, 38(6), 476–485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kazdin AE, Siegel TC, & Bass D (1990). Drawing on clinical practice to in form research on child and adolescent psychotherapy: survey of practitioners. Professional Psychology: Research and Practice, 21(3), 189–98. 10.1037//0735-7028.21.3.189 [DOI] [Google Scholar]
  48. Kearns MA, & Hawley KM (2014). Predictors of polypharmacy and off-label prescribing of psychotropic medications: A national survey of child and adolescent psychiatrists. Journal of Psychiatric Practice, 20(6), 438–447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Kendall PC, & Beidas RS (2007). Smoothing the trail for dissemination of evidence-based practices for youth: Flexibility within fidelity. Professional Psychology: Research and Practice, 38(1), 13–20. 10.1037/0735-7028.38.1.13 [DOI] [Google Scholar]
  50. Lambert MJ, Whipple JL, Hawkins EJ, Vermeersch DA, Nielsen SL, & Smart DW (2003). Is it time for clinicians to routinely track patient outcome? A meta-analysis. Clinical Psychology: Science and Practice, 10(3), 288–301. 10.1093/clipsy/bpg025 [DOI] [Google Scholar]
  51. Lonigan CJ, Elbert JC, & Johnson SB (1998). Empirically supported psychosocial interventions for children: An overview. Journal of Clinical Child Psychology, 27(2), 138–145. 10.1207/s15374424jccp2702 [DOI] [PubMed] [Google Scholar]
  52. Lumley T (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(1), 1–19. 10.18637/jss.v009.i08 [DOI] [Google Scholar]
  53. Mash EJ, & Hunsley J (2005). Evidence-Based Assessment of Child and Adolescent Disorders: Issues and Challenges. Journal of Clinical Child & Adolescent Psychology, 34(3), 362–379. 10.1207/s15374424jccp3403_1 [DOI] [PubMed] [Google Scholar]
  54. Muthén LK, & Muthén BO (2012). Mplus User’s Guide (7th ed.). Muthén & Muthén; Retrieved from https://www.statmodel.com/download/usersguide/MplususerguideVer_7_r3_web.pdf [Google Scholar]
  55. Nelson TD, & Steele RG (2007). Predictors of practitioner self-reported use of evidence-based practices: Practitioner training, clinical setting, and attitudes toward research. Administration and Policy in Mental Health and Mental Health Services Research, 34(4), 319–330. 10.1007/s10488-006-0111-x [DOI] [PubMed] [Google Scholar]
  56. Nelson TD, Steele RG, & Mize JA (2006). Practitioner attitudes toward evidence-based practice: Themes and challenges. Administration and Policy in Mental Health and Mental Health Services Research, 33(3), 398–409. 10.1007/s10488-006-0044-4 [DOI] [PubMed] [Google Scholar]
  57. Rogers EM (2010). Diffusion of Innovations. New York: Routledge. [Google Scholar]
  58. Roth A, & Fonagy P (2005). What works for whom: A critical review of psychotherapy research (2nd ed.). New York: Guildford Publications. [Google Scholar]
  59. Schoenwald SK, & Hoagwood K (2001). Effectiveness, transportability, and dissemination of interventions: what matters when? Psychiatric Services, 52(9), 1190–1197. 10.1176/appi.ps.52.9.1190 [DOI] [PubMed] [Google Scholar]
  60. Sheehan AK, Walrath CM, & Holden EW (2006). Evidence-based Practice Use, Training and Implementation in the Community-Based Service Setting: A Survey of Children’s Mental Health Service Providers. Journal of Child and Family Studies, 16(2), 169–182. 10.1007/s10826-006-9076-3 [DOI] [Google Scholar]
  61. Silverman WK, & Hinshaw SP (2008). The Second Special Issue on Evidence-Based Psychosocial Treatments for Children and Adolescents: A 10-Year Update. Journal of Clinical Child & Adolescent Psychology, 37(1), 1–7. 10.1080/15374410701817725 [DOI] [PubMed] [Google Scholar]
  62. Task Force on Promotion and Dissemination of Psychological Procedures. (1995). Training in and dissemination of empirically-validated psychological treatment: Report and recommendations. The Clinical Psychologist, 48(1), 2–23. 10.1037/e554972011-003 [DOI] [Google Scholar]
  63. The National Advisory Mental Health Council Workgroup on Child and Adolescent Mental Health Intervention Development and Deployment. (2001). Blueprint for Change: Research on Child and Adolescent Mental Health. Washington, D.C. Retrieved from https://www.nimh.nih.gov/about/advisory-boards-and-groups/namhc/reports/blueprint-for-change-research-on-child-and-adolescent-mental-health.shtml [Google Scholar]
  64. Walrath CM, Sheehan AK, Holden EW, Hernandez M, & Blau G (2006). Evidence-based treatments in the field: A brief report on provider knowledge, implementation, and practice. The Journal of Behavioral Health Services & Research, 33(2), 244–253. 10.1007/s11414-005-9008-9 [DOI] [PubMed] [Google Scholar]
  65. Weersing VR, Weisz JR, & Donenberg GR (2002). Development of the Therapy Procedures Checklist: A Therapist-Report Measure of Technique Use in Child and Adolescent Treatment. Journal of Clinical Child and Adolescent Asychology, 31(2), 168–180. 10.1207/S15374424JCCP3102 [DOI] [PubMed] [Google Scholar]
  66. Weisz J, Bearman SK, Santucci LC, & Jensen-Doss A (2017). Initial Test of a Principle-Guided Approach to Transdiagnostic Psychotherapy With Children and Adolescents. Journal of Clinical Child & Adolescent Psychology, 46(1), 44–58. 10.1080/15374416.2016.1163708 [DOI] [PubMed] [Google Scholar]
  67. Weisz JR, Chorpita BF, Palinkas LA, Schoenwald SK, Miranda J, Bearman SK, … The Research Network on Youth Mental Health. (2012). Testing Standard and Modular Designs for Psychotherapy Treating Depression, Anxiety, and Conduct Problems in Youth: A Randomized Effectiveness Trial. Archives of General Psychiatry, 69(3), 274–282. 10.1001/archgenpsychiatry.2011.147 [DOI] [PubMed] [Google Scholar]
  68. Weisz JR, Jensen-Doss A, & Hawley KM (2006). Evidence-based youth psychotherapies versus usual clinical care: a meta-analysis of direct comparisons. The American Psychologist, 61(7), 671–689. 10.1037/0003-066X.61.7.671 [DOI] [PubMed] [Google Scholar]
  69. Weisz JR, Kuppens S, Eckshtain D, Ugueto AM, Hawley KM, & Jensen-Doss A (2013). Performance of Evidence-Based Youth Psychotherapies Compared With Usual Clinical Care. JAMA Psychiatry, 70(7), 750 10.1001/jamapsychiatry.2013.1176 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wolk CB, Marcus SC, Weersing VR, Hawley KM, Evans AC, Hurford MO, & Beidas RS (2016). Therapist- and Client-Level Predictors of Use of Therapy Techniques During Implementation in a Large Public Mental Health System. Psychiatric Services, 67(5), 551–557. 10.1176/appi.ps.201500022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Youngstrom EA, Choukas-Bradley S, Calhoun CD, & Jensen-Doss A (2015). Clinical guide to the evidence-based assessment approach to diagnosis and treatment. Cognitive and Behavioral Practice, 22(1). 10.1016/j.cbpra.2013.12.005 [DOI] [Google Scholar]

RESOURCES