Supervision Training in Graduate Programs in Psychology: Moving from Conceptual to Procedural

Supervision Training in Graduate Programs in Psychology: Moving from Conceptual to Procedural | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Supervision Training in Graduate Programs in Psychology: Moving from Conceptual to Procedural Sarah Quintal, Mark Terjesen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9247979/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Clinical supervision is recognized as both a foundational and functional competency in the training of psychologists (Rodolfa et al., 2005 ), with exposure to supervision knowledge emphasized in American Psychological Association (APA) accreditation requirements. However, foundational knowledge does not necessarily translate into effective supervisory skill. Falender (2018) highlighted supervision training as “inadequately addressed” within many psychology curricula. From a Rational Emotive Cognitive-Behavioral Therapy (RE-CBT) perspective, supervision emphasizes structured, skill-based, and competency-focused training, offering a useful lens for evaluating current practices. The present study examined how competency-based supervision training is integrated within APA-accredited health service psychology doctoral programs, specifically whether training emphasizes conceptual knowledge, procedural application, or both, and whether it varies by program type and population focus. A systematic content analysis was conducted on supervision course syllabi from APA-accredited doctoral programs across the United States. Of 415 programs contacted, 67 submitted syllabi (≈ 16%). Syllabi were coded using a structured rubric assessing course features, experiential training, competency evaluation, multicultural content, and population focus. Composite indices of supervision training rigor and intensity were derived to capture the integration of conceptual and procedural elements. Post-hoc independent samples t-tests were conducted to examine differences in supervision training characteristics by clinical orientation. Results indicated that supervision training was primarily procedural, with most programs including experiential components. Nearly all programs required a supervision course and included competency evaluation; however, fewer syllabi documented in vivo supervision or population-specific applied experiences. No significant differences in rigor or intensity were found across program types, and exploratory independent samples t-tests examining CBT-oriented versus non-CBT-oriented programs did not reveal significant differences in supervision training rigor or intensity. Training focused on children and adolescents was less frequently represented than adult populations. Findings are discussed in relation to APA supervision competencies and RE-CBT-informed supervision practices, with recommendations for strengthening supervision curricula. clinical supervision RE-CBT APA-accredited health service psychology Introduction Clinical supervision has consistently been recognized as a central component of graduate training and professional development across health service psychology programs. It has been designated a distinct professional competency requiring specialized education and training since the American Psychological Association (APA) Competencies Conference in 2002 and the National Council of Schools of Professional Psychology Mission Bay Conference (Bourg et al., 1989; Falender et al., 2004; Kaslow et al., 2004). It has also been a requirement for APA-accredited programs since 1996 (APA, 1996). In fact, supervision is the third most common professional activity performed by psychologists, surpassed only by psychotherapy and psychological assessment (Norcross & Rogan, 2013). Defined broadly, supervision involves a collaborative relationship in which a senior professional supports the development of a trainee’s clinical skills, ethical decision-making, professional identity, and reflective practice (Falender & Shafranske, 2004). Whereas most doctoral training programs for clinical psychology in the United States include content focused on developing student competencies in delivering CBT (Heatherington et al., 2012), supervision training appears substantially less universal and less emphasized in formal curricula. More broadly, training literature has identified persistent gaps in preparing psychologists to deliver culturally responsive and competency-based care, underscoring the need for intentional, developmentally informed training practices that extend beyond technical skill acquisition (Benuto et al., 2019). Within this broader literature, CBT and REBT provide especially relevant frameworks for supervision because they emphasize structured, collaborative, and skill-based learning. CBT supervision has been described as active, agenda-driven, and competency-focused, with attention to case conceptualization, feedback, and behavioral rehearsal (Milne & Reiser, 2017). Similarly, REBT-informed supervision emphasizes identifying beliefs that may interfere with clinical work while promoting flexible thinking and active skill development. Accordingly, RE-CBT offers a useful lens for examining how supervision is operationalized in doctoral training. In the context of school and clinical psychology, supervision is uniquely complex. Psychologists often operate in interdisciplinary settings, support diverse populations, and navigate complex legal and ethical frameworks related to schools, families, healthcare systems, and broader community contexts. Effective supervision is therefore critical to preparing psychologists to meet these multifaceted demands. Supervision must also reflect the realities of working with children, adolescents, their caregivers, and adults, particularly in cases involving behavioral or emotional disturbances. However, emerging evidence suggests that current supervision training in school and clinical psychology programs may not be adequately preparing future supervisors or clinicians for these specific challenges (Falender, 2018; Newman, 2018). As such, this article reviews APA accreditation requirements related to supervision and synthesizes current empirical research on supervision training in health service psychology, with particular attention to school, clinical, and counseling psychology contexts. APA Guidelines for Clinical Supervision in Health Service Psychology The American Psychological Association (APA) recognizes supervision as a foundational and functional competency in its competency benchmarks (APA, 2025; Rodolfa et al., 2005). The APA Guidelines on Supervision outline six domains that define effective, ethical, and competency-based supervision in health service psychology. These domains emphasize the need for supervisors to maintain current clinical and supervisory competence; integrate multicultural humility and responsiveness; build strong supervisory alliances; utilize structured teaching, observation, and feedback strategies; manage problems of professional competence through clear evaluation and remediation processes; as well as uphold ethical, legal, and regulatory responsibilities through clear communication and documentation. Together, the Guidelines provide a blueprint designed to promote high-quality, culturally informed supervision that protects clients and supports supervisee development (APA, 2025). Building on this framework, the Competency Benchmarks model developed by Fouad and colleagues (2009) proposed a developmental approach such that competence develops progressively and requires intentional training, systematic assessment, and clear performance expectations, suggesting the need for explicit instruction and evaluation of supervision-related competencies within graduate education (Fouad et al., 2009). Together, the APA Guidelines on Supervision and the Competency Benchmarks model emphasizes supervision as a distinct, competency-based professional practice that requires intentional, developmentally sequenced training and systematic evaluation (APA, 2025; Fouad et al., 2009). These competency-based principles overlap closely with RE-CBT supervision, which emphasizes clear goals, structured teaching, and repeated opportunities to translate knowledge into practice. Examining whether supervision curricula include procedural and skill-based elements is therefore relevant not only to supervision broadly, but also to CBT- and REBT-informed supervision. By articulating clear expectations for supervisor competence, multicultural humility, ethical practice, and assessment across training levels (Stoltenberg & McNeill, 2010; Watkins et al., 2019), these frameworks provide a strong foundation for high-quality supervision in health service psychology. However, the extent to which these standards are consistently reflected in graduate training curricula remains an empirical question, highlighting the importance of examining how supervision is taught and operationalized within APA-accredited programs. Given that practicing supervision requires training in supervision and the assessment of supervision competencies (Bernard & Goodyear, 2014), these frameworks also imply the need for intentional curriculum design that supports competent supervisory practice amongst trainees. Models of Clinical Supervision in Psychology Supervision practice evolved from an apprenticeship model and psychotherapy theory. It was assumed that clinical knowledge and skills derived from psychotherapy models were directly transferable to supervision practice (Falender & Shafranske, 2004). As supervision has been increasingly recognized as a distinct professional competency (APA, 2025), a number of theoretical models has emerged to guide how supervision is conceptualized, taught, and evaluated. Some of the most prominent models are outlined below. Developmental Model. Developmental models conceptualize supervision as a process that evolves with the supervisee's professional maturity. One widely cited framework is the Integrated Developmental Model (IDM), which outlines progressive stages of competence across clinical, conceptual, and interpersonal domains (Stoltenberg & McNeill, 2010). Supervision within this model is tailored to the supervisee’s level, with increasing autonomy and complexity over time. The IDM further emphasizes that supervisees progress through developmental levels characterized by changes in self-awareness, motivation, and perceived independence. As such, supervisors are required to adjust their supervisory responsibilities by incorporating structured feedback, goal setting, and changing roles to support growth (Stoltenberg & McNeill, 2010). Discrimination Model. The Discrimination Model (Bernard, 1979) is one of the most widely used supervision models in training programs. It encourages supervisors to shift roles, as teacher, counselor, or consultant, based on the supervisee’s needs, as well as specific skill related to intervention and case conceptualization. This model has been favored for its flexibility and applicability to diverse training contexts. Developmental, Ecological, Problem-Solving (DEP) Model. Widely used in school psychology, the DEP model tailored supervision training to supervisee readiness as it relates to schools, families, interdisciplinary teams, and organizational systems. Emphasizing collaborative problem-solving, reflective practice, and skill scaffolding, the DEP model supports increasing autonomy while maintaining client care and system-level effectiveness (Simon et al., 2014; Simon & Swerdlik, 2022). Competency-Based Models. The Competency-Based Supervision Model (Falender & Shafranske, 2004, 2007) aligns closely with APA’s emphasis on evidence-based practice and guidelines. This model encourages supervisors to explicitly teach and evaluate competencies across areas such as ethics, diversity, legal issues, and assessment. It serves as a bridge between conceptual knowledge and procedural application, a particularly important feature given the gaps in experiential training identified in many supervision courses. Vertical Supervision Model. Vertical supervision involves a hierarchical training system in which more experienced clinicians supervise less experienced trainees. Meta-supervision may be included in this model, whereby supervisors evaluate the supervisee’s development and competence as a supervisor. Research suggests this model can enhance reflective practice and self-efficacy when supervisors model reflection and support self-assessment (Curtis et al., 2016). Keenan-Miller and Corbett (2015) found that advanced doctoral students were safe and effective supervisors when supported through meta-supervision (Keenan-Miller & Corbett, 2015). Although these models provide important frameworks for supervision practice, other approaches rooted in psychotherapy theory, such as cognitive-behavioral supervision incorporating experiential rehearsal and role-play (Milne & Reiser, 2017), as well as cognitive, psychodynamic, and family therapy–based supervision models (Beck et al., 2008), also remain influential. CBT- and REBT-informed supervision models are highly relevant to contemporary training, as they emphasize structured, collaborative, and feedback-oriented approaches, including case conceptualization, behavioral rehearsal, and the examination of therapist beliefs to promote cognitive flexibility and skill development (Milne & Reiser, 2017). However, psychotherapy-derived models alone may insufficiently address core supervisory processes, including continuous evaluation of supervisee development, the supervisory relationship, multicultural training, ethical and legal responsibilities, as well as supervisee competence (Falender, 2018). Thus, CBT- and REBT-informed approaches are especially useful for evaluating whether supervision curricula extend beyond conceptual instruction to include the experiential and procedural components central to effective supervision training. Although these models inform how supervision is structured, taught, and evaluated in graduate programs, the extent to which they are implemented consistently across curricula remains in question. Empirical Research on Supervision Training In line with APA accreditation requirements, graduate programs in health service psychology are expected to provide training in supervision, including both conceptual knowledge and applied skills (APA, 2025). However, recent critiques have highlighted significant variability and limitations in how supervision training is implemented (Falender, 2018; Newman, 2018). Although empirical research indicates that most APA-accredited doctoral programs include some didactic instruction in supervision, many programs emphasize theoretical content while offering limited experiential opportunities for students to apply supervision principles in practice (Kaslow et al., 2007; Falender & Shafranske, 2004). This typically includes assigned readings and/or videos, and theoretical content focused on supervision models and ethics. However, the depth and consistency of this instruction vary considerably between programs. In a national survey of training directors, Newman et al., (2018) found that while most programs report offering supervision training, only a small percentage provide structured curricula with both conceptual and applied components. The majority of programs rely on lecture-based instruction, with minimal opportunities for supervisees to practice supervision skills in real or simulated environments. This pattern reflects earlier findings indicating that formal supervision training has historically been inconsistently embedded in graduate education. Further, surveys of training directors conducted in 2000 found that fewer than one-third of training programs included supervision training at that time and surveys on supervision practice by training directors have not been conducted since then (Scott et al., 2000). Additional evidence highlights that relatively few practicing supervisors report having received formal supervision coursework, with fewer than 20% endorsing such training in one national sample (Peake et al., 2002). Similarly, Kaslow and colleagues (2005) found that many training directors in U.S. psychology graduate programs did not view supervision training as a top priority compared to other areas of training, suggesting a gap between how important supervision is to the profession and how much emphasis it actually receives in training programs. The lack of procedural or experiential training in supervision is a recurring concern in the literature. Falender (2018) has argued that “inadequate attention” is given to training that allows students to actively engage in supervision tasks, such as conducting mock supervision sessions, engaging in live supervision, or receiving feedback on their supervisory practice. This gap is particularly problematic given that supervision is a learned skill that cannot be fully acquired through reading or discussion alone. In the few studies that have examined applied supervision training, findings suggest that programs incorporating experiential components see improved supervisee confidence and competence (Sterner, 2009; Ladany et al., 2001). Such components are essential in preparing future psychologists for leadership, consultation, and training roles; responsibilities that school psychologists often assume in their professional practice. Consistent with these concerns, Crook-Lyon and colleagues (2008) reported in a national survey of 233 U.S. predoctoral interns that only 39% had completed a graduate-level course in supervision. Although these foundational studies provide important historical context, many were conducted over two decades ago, and supervision training practices may have evolved in response to updated APA guidelines, competency-based frameworks, and increased emphasis on experiential training; however, the extent of such changes remains unclear, underscoring the need for contemporary empirical examination. Research has also highlighted a lack of specificity in supervision training related to population diversity. Few programs provide targeted supervision training for work involving children, adolescents, and families, despite the high prevalence of such cases in school and clinical settings (Accurso et al., 2011). This gap is particularly concerning given the diverse populations served by psychologists. For instance, child and adolescent clients, especially those with disruptive behavior disorders, represent one of the most frequently encountered groups in clinical settings (Accurso et al., 2011). Supervising clinical work with these populations requires specialized knowledge and competencies, both for the supervisee and the supervisor. A generalized or “one size fits all” approach to supervision training may inadequately prepare graduate students for these challenges. Therefore, examining the current state of supervision training, including its scope, depth, and specificity is essential to informing curriculum development and improving service quality and trainee development. The literature to date highlights several important conclusions regarding supervision in psychology training. Supervision is recognized as a critical competency, yet its instruction is often inconsistent across programs. While conceptual instruction in supervision is more common, experiential training is less frequently provided, despite evidence that applied practice enhances supervisory effectiveness. Although theoretical models such as the Integrated Developmental Model (IDM) and the Discrimination Model are referenced in training, their practical application remains unknown. Moreover, population-specific supervision training, particularly for children, adolescents, and families is largely absent or not explicitly addressed. These gaps are especially pronounced in school psychology, a field grounded in applied, systems-level practice. Supervisor perspectives further suggest that these training gaps may have lasting effects. In a national study of supervisors, Nelson and Graves (2011) found that many supervisors perceived advanced trainees as underprepared in core professional competencies, indicating a mismatch between graduate training experiences and the level of competence expected for independent practice. Relatedly, Furr and Brown-Rice (2016) found that doctoral students demonstrated limited knowledge of educators’ problems of professional competency, raising concerns about trainees’ preparedness to identify, evaluate, and respond to competency concerns, skills that are central to effective supervision. While the field has made strides in conceptualizing supervision as a measurable and teachable competency, much of the existing research is descriptive or survey-based, relying on self-report data from faculty or training directors. There is a lack of longitudinal, experimental, or mixed-methods studies examining the impact of different training approaches on supervisee outcomes. Further, the literature is heavily concentrated in clinical and counseling psychology, with limited focus on supervision training within school psychology programs. This is a significant oversight given the unique demands of school-based practice and the diversity of clients served. Despite the clarity of the newly revised APA guidelines, little is known about how APA-accredited health service psychology programs actually teach supervision to graduate students who will become future psychologists. This study is needed for several key reasons. First, supervision is a core professional competency of mental health training; therefore, understanding how programs prepare trainees for supervisory roles is essential. Second, because supervisors carry the highest duty to protect clients and the public, evaluating supervision training is critical for ensuring client welfare. Third, the Guidelines are aspirational rather than prescriptive, creating significant variability in how programs interpret and implement supervision training. As such, systematic examination can identify inconsistencies, gaps, and areas of strength across programs. Fourth, the Guidelines highlight the need for supervision that integrates a multicultural orientation, yet it remains unclear how consistently programs embed cultural humility and opportunities into supervision curricula. Fifth, the Guidelines call for research linking supervision practices to client outcomes and understanding how supervision is taught is a necessary foundation for such investigations. Sixth, existing literature suggests many licensed psychologists feel underprepared to supervise, strengthening the need to examine whether supervision training at the graduate level is sufficient and evidence based. Seventh, the Guidelines are intended to be a living document, updated based on emerging evidence. Comprehensive data on training practices across program types are essential to inform future revisions. Finally, strengthening supervision training is vital to the future of the profession, as high-quality supervision is directly tied to therapist competence, ethical practice, and client care. By evaluating how APA-accredited health service psychology programs train future psychologists in supervision and examining whether supervision training characteristics differ across clinical, school, and counseling psychology programs, this study directly responds to the priorities articulated in the Guidelines and contributes to improving training quality, enhancing public protection, and advancing the science and practice of clinical supervision. These concerns are especially salient from a CBT/REBT perspective, as RE-CBT supervision emphasizes modeling, feedback, guided discovery, and rehearsal rather than theoretical discussion alone (Milne & Reiser, 2017). Thus, the extent to which doctoral supervision curricula include structured experiential training is highly relevant to their alignment with RE-CBT-informed supervision. The Current Study The current study used a content analysis of supervision syllabi from APA-accredited doctoral programs across the United States to assess the degree to which conceptual and procedural components are integrated into supervision training. Specifically, the study examined supervision training in APA-accredited health service psychology programs by investigating the extent to which curricula include conceptual (didactic/theoretical) and procedural (experiential/practical) components. The first research question examined how supervision training is operationalized and evaluated in APA-accredited programs overall, including the specific supervision training features present across programs. In addition, the study examined whether supervision training characteristics differ systematically across program emphases, clinical psychology, school psychology, and counseling psychology. One-way analyses of variance (ANOVAs) were used to examine differences in supervision training rigor and intensity across program types. In addition, exploratory independent samples t-tests were conducted to examine whether supervision training characteristics differed between CBT-oriented and non-CBT-oriented programs. It was hypothesized that most APA-accredited programs would emphasize conceptual supervision training, including readings, theoretical frameworks, and discussions of supervisory models, whereas fewer programs would include procedural or experiential components such as in vivo supervision, role plays, or applied supervision exercises. Given inconsistencies in the existing literature, it was further hypothesized that supervision training rigor and supervision intensity may differ across program types, operationalized as indices reflecting the integration and depth of supervision training. Exploratory analyses were expected to provide preliminary insight into whether CBT-oriented programs demonstrate greater procedural emphasis or structured training features compared to non-CBT programs. By systematically analyzing supervision syllabi and comparing training characteristics across program types, this study aimed to provide data-driven insights into current supervision training practices and inform evidence-based recommendations for curricular improvement. More specifically, the study offers preliminary insight into whether contemporary supervision curricula reflect features consistent with RE-CBT supervision models, particularly structure, procedural emphasis, and competency-based training. Method Search Strategy This study utilized a systematic content analysis methodology to examine the extent and nature of supervision training within APA-accredited health service psychology doctoral programs. The primary data source consisted of course syllabi from doctoral-level courses that explicitly addressed clinical supervision. The study sought to evaluate the balance between conceptual and procedural supervision training, as well as the extent to which supervision training incorporated applied competency evaluation, remediation processes, multicultural considerations, and population-specific supervision content. These variables were subsequently operationalized into quantitative indices of supervision training rigor and intensity. Content analysis is an appropriate methodology for systematically identifying patterns, themes, and frequencies of content elements in textual documents such as course syllabi (Krippendorff, 2018 ). Initial search. An extensive search of APA-accredited health service psychology programs was conducted using the official APA Accreditation website ( https://accreditation.apa.org/accredited-programs ). The search began by selecting “Start Your Search” and applying the following criteria: doctoral-level programs, all U.S. states, selected substantive areas including Clinical Psychology, Counseling Psychology, and School Psychology, and all doctoral degrees (PhD, PsyD). This strategy was designed to identify the full population of APA-accredited health service psychology doctoral programs and to capture variability in supervision training practices across program types and training models. There was a total of 415 programs identified and contacted. 67 programs provided their supervision course syllabi (n ≈ 16%). The response rate of approximately 16% is relatively low and represents a potential source of non-response bias. Programs that elected to share syllabi may differ systematically from those that did not, such as having more developed or structured supervision training. As a result, findings may overrepresent programs with stronger or more formalized supervision curricula and should be interpreted with this context in mind. Secondary search. Following the initial identification of APA-accredited programs, a secondary search was conducted to obtain supervision-related syllabi. Using the list of eligible programs, each program’s official university website was reviewed to identify key personnel, including program directors, directors of clinical training, department chairs, or faculty members teaching supervision courses. These individuals were contacted via email to explain the purpose of the study and request copies of supervision-focused syllabi, including didactic supervision courses and supervision practica. When initial contact attempts were unsuccessful, follow-up emails were sent to maximize response rates. Inclusion and Exclusion Criteria The author and a team of graduate research assistants (RAs) initially reviewed each program listing and associated course materials to determine eligibility based on the following criteria. Courses were included if they (a) were primarily focused on supervision rather than general clinical practice, ethics, or other topics, and (b) were offered within a doctoral-level APA-accredited program. Courses were excluded if they did not explicitly address supervision content or were part of non-APA-accredited programs, undergraduate programs, master’s-level or certificate-only programs. Additionally, courses were excluded if syllabi or sufficient course documentation could not be obtained after reasonable efforts to contact program personnel. This approach ensures that only doctoral-level, supervision-specific coursework was evaluated. Evaluation Criteria A structured coding framework was developed to extract relevant information from each syllabus. The framework was informed by the APA Guidelines for Clinical Supervision in Health Service Psychology (APA, 2025), competency-based supervision literature (Falender & Shafranske, 2004 ), and a pilot review of a subset of syllabi. After finalizing the sample, each syllabus was coded on a set of structured variables capturing course structure, instructional features, experiential training components, and competency evaluation practices. Course Structure. Four variables described the structure and instructional scope of each course: (1) total number of instructional weeks in the course (excluding holidays or weeks with no class), (2) number of weeks explicitly devoted to supervision instruction, (3) number of required readings, and (4) number of required videos or multimedia learning components. The number of required readings was coded as the total count of all required instructional materials explicitly listed on the syllabus, including textbooks and peer-reviewed journal articles. Each required textbook chapter and each required article was coded as one reading. Recommended or optional materials were excluded from this count. Supervision Intensity Index. A composite supervision intensity index was calculated to represent the proportion of course time devoted to supervision content. This index was computed as the ratio of supervision-specific instructional weeks to total course weeks (Supervision Weeks ÷ Total Weeks), yielding a continuous value ranging from 0 to 1, with higher values indicating greater emphasis on supervision within the course. Instructional and Training Features. Several variables assessed key instructional features of supervision training. Meta-supervision was coded as present when the syllabus included structured opportunities for supervision of supervision, including faculty-led meta-supervision or organized peer supervision activities. Clinical focus was coded as present when supervision training was framed around clinical service delivery, including psychotherapy or related health service psychology practices. Supervision model was coded based on the primary clinical orientation used to frame supervision practice. Courses that emphasized supervision across multiple psychotherapy orientations (e.g., CBT, psychodynamic, REBT) or explicitly allowed students to integrate multiple approaches were coded as integrative. CBT or psychodynamic supervision was coded only when the syllabus explicitly privileged that orientation as the primary supervisory framework. Competency Evaluation. The remaining variables evaluated how supervisory competence was assessed and remediated. Competency evaluation was coded as present when the syllabus included explicit evaluation of supervisory competence. Evaluation method was coded based on whether competence was assessed via written methods (e.g., exams, papers), performance-based methods (e.g., case presentations), or both. Remediation was coded as present when the syllabus specified any academic or programmatic action taken if a student failed the course or failed to meet competency standards, including course repetition, delayed progression, remediation, or dismissal. Supervision Training Rigor Index. A composite supervision training rigor index was created by summing key instructional and evaluation features, including the presence of meta-supervision, clinical focus, competency evaluation, experiential training, multicultural supervision, and remediation procedures (each coded as 0 = absent, 1 = present). Scores were summed to yield a total rigor score, with higher values reflecting greater integration of conceptual, procedural, and competency-based training components. The resulting scale ranged from 0 to the total number of included features, representing increasing levels of training rigor. All syllabi were coded using this rubric to ensure consistency in data extraction and analysis. Data Collection and Analysis Data were extracted from each eligible syllabus and coded on all evaluation variables using Microsoft Excel. The dataset was subsequently imported into JASP for data cleaning, descriptive analyses, and interrater reliability calculations, as well as the computation of composite supervision intensity and supervision training rigor indices. Coding Approach. The author and a team of trained RAs coded each syllabus according to the established evaluation criteria. The RAs were all doctoral-level school psychology students trained in systematic coding procedures. Each rater participated in a group training session with the author, an individual training meeting, and received a detailed coding manual outlining the operational definitions of each variable, examples of coding decisions, and decision rules for each variable. During the training phase, all coders jointly coded ten syllabi with the author to establish interrater reliability, aiming for a minimum Cohen’s Kappa of 0.80. Once this reliability threshold was reached, the RAs proceeded to code the remaining syllabi. In cases where discrepancies occurred that reduced reliability below this threshold, the raters met with the author to discuss and resolve differences, and the consensus decision was recorded on the master coding sheet. All coding were based on the full content of each syllabus, including course descriptions, objectives, required readings and media, assignments, supervision orientation statements, and competency evaluation procedures. When relevant information was unclear, coders documented the missing data, and clarification was sought from the program contact when feasible. Throughout the data collection and coding process, the author held regular meetings with the research team to review progress, discuss discrepancies, and ensure consistency across raters. Consensus meetings were used to refine definitions and resolve ambiguities, enhancing the reliability and validity of the dataset. The finalized coded dataset was analyzed descriptively to identify trends and patterns across programs in the inclusion and structure of supervision training. To examine whether supervision training intensity and supervision training rigor differed by discipline, one-way analyses of variance (ANOVAs) were conducted comparing clinical, school, and counseling psychology programs on the composite indices. Additionally, independent samples t-tests were conducted as exploratory analyses to examine differences in supervision training intensity and rigor between programs that explicitly endorsed a CBT orientation and those that did not. Results Table 1 Cohen’s Kappa for Inter-rater Reliability. Variable Cohen’s Kappa Course Required 1.000 Meta-Supervision 0.881 Clinical Focus 0.849 Clinical Orientation 0.745 Competency Eval Present 0.660 Evaluation Method 0.798 Experiential Training Present 0.684 Experiential Training Type 0.902 Procedural Emphasis 0.891 Multicultural Supervision 0.894 Population Specific 0.873 Remediation Outlined 0.489 Cohen’s kappa values demonstrated overall strong interrater reliability across coded variables (κ = .66–1.00). A few variables showed lower agreement, most notably remediation outlined (κ = .49) (Table 1 ). As shown in Table 2 , the majority of the 67 APA-accredited health service psychology programs were clinical psychology programs ( n = 38, 56.7%), followed by school ( n = 15, 22.4%) and counseling ( n = 13, 19.4%) programs, with few combined programs ( n = 1, 1.5%). Most programs awarded a PhD ( n = 55, 82.1%), compared to a smaller proportion offering a PsyD ( n = 12, 17.9%). Table 2 Program Type and Degree Frequencies. Program Type n % Clinical 38 56.7 School 15 22.4 Counseling 13 19.4 Combined 1 1.5 Degree Type n % PhD 55 82.1 PsyD 12 17.9 Descriptive statistics for supervision training variables are presented in Table 3 . Supervision intensity across programs was relatively high on average ( M = 0.76, SD = 0.24). Programs included a mean of 12.34 total weeks ( SD = 3.44) of supervision-related content, with an average of 9.18 weeks ( SD = 3.79) specifically devoted to supervision. With respect to didactic components, programs assigned an average of 24.60 readings ( SD = 17.02) and 1.03 videos ( SD = 2.47). Table 3 Descriptive Statistics for Supervision Training Variables. Supervision Intensity M SD Minimum Maximum 0.761 0.238 0.000 1.000 Total Weeks 12.343 3.440 2.000 23.00 Supervision Weeks 9.179 3.794 0.000 15.00 Num. Readings 24.597 17.021 0.000 78.00 Num. Videos 1.030 2.468 0.000 14.00 Course Requirements and Orientation. Nearly all programs required a formal supervision course ( n = 66, 98.5%). A clinical focus was identified in the majority of programs ( n = 51, 76.1%); however, specific theoretical orientations were often not specified ( n = 35, 52.2%). When identified, cognitive behavioral therapy was the most frequently endorsed orientation ( n = 19, 28.4%), followed by integrative approaches ( n = 11, 16.4%) and psychodynamic orientations ( n = 2, 3.0%). Evaluation and Accountability. Competency-based evaluation was present in nearly all programs ( n = 66, 98.5%). Most programs employed multiple evaluation methods ( n = 45, 67.2%), whereas fewer relied solely on written examinations ( n = 20, 29.9%) or case presentations alone ( n = 1, 1.5%). Additionally, the vast majority of programs explicitly outlined remediation procedures ( n = 66, 98.5%). Approximately half of programs also incorporated meta-supervision components ( n = 33, 49.3%). Experiential and Procedural Training. Experiential training components were included in most programs ( n = 62, 92.5%). Of these, more than half utilized multiple experiential methods ( n = 37, 55.2%), while others relied solely on role-play or simulation ( n = 20, 29.9%) or live/in vivo supervision alone ( n = 5, 7.5%). A small subset did not specify experiential methods ( n = 5, 7.5%). In terms of training emphasis, most programs prioritized procedural skills ( n = 35, 52.2%), with fewer programs adopting a mixed conceptual–procedural approach ( n = 29, 43.3%) or a conceptual-only emphasis ( n = 3, 4.5%). Diversity and Population Focus. All programs included content related to multicultural supervision ( n = 67, 100%). However, a greater proportion of programs emphasized adult populations ( n = 46, 68.7%) compared to child and adolescent populations ( n = 21, 31.3%). Table 4 Frequencies and Percentages of Supervision Course Features and Training Components. Training Feature n % Course Required Required 66 98.5 Clinical Focus & Orientation Clinical focus present 51 76.1 CBT 19 28.4 Psychodynamic 2 3.0 Integrative 11 16.4 Not Specified 35 52.2 Evaluation & Accountability Competency Evaluation Present 66 98.5 Written/Exam Only 20 29.9 Case Presentation Only 1 1.5 Both 45 67.2 Remediation Procedures Outlined 66 98.5 Meta-Supervision Included 33 49.3 Experiential & Procedural Training Experiential Training Present 62 92.5 Role Play/Simulation Only 20 29.9 Live/In Vivo Only 5 7.5 Multiple Methods 37 55.2 Not Specified 5 7.5 Procedural Emphasis Conceptual Only 3 4.5 Procedural Only 35 52.2 Mixed 29 43.3 Diversity & Population Focus Multicultural Supervision Present 67 100 Adult Focus 46 68.7 Child/Adolescent Focus 21 31.3 As shown in Tables 5 and 6 , one-way analyses of variance (ANOVAs) were conducted to examine whether supervision intensity and supervision training rigor differed across clinical, school, and counseling psychology programs. For both analyses, the assumption of homogeneity of variances was met (supervision intensity: F (2, 63) = 0.92, p = .403; supervision rigor: F (2, 63) = 1.55, p = .220). Results indicated that neither supervision intensity, F (2, 63) = 0.35, p = .705, partial η² = .01, nor supervision training rigor, F (2, 63) = 0.90, p = .411, partial η² = .03, differed significantly by program type. Descriptive statistics showed comparable levels of supervision intensity and rigor across program types. Although these findings were not statistically significant, the small effect sizes (partial η² = .01 and .03) suggest minimal practical differences across program types. However, it is important to consider that the present study may have been underpowered to detect moderate effects, given the relatively small and uneven group sizes (n = 13–38 per group). As such, the absence of statistically significant differences should be interpreted with caution, as meaningful differences may not have been detectable with the available sample size. As depicted in Tables 7 and 8 , exploratory analyses were conducted to examine whether CBT-oriented programs differed from non-CBT-oriented programs on supervision intensity and supervision training rigor. For supervision intensity, an independent-samples t test indicated no significant difference between CBT-oriented and non-CBT-oriented programs, t (64) = 0.03, p = .980, Cohen’s d = 0.01. The assumption of homogeneity of variances was met, F (1, 64) = 0.02, p = .894. Descriptive statistics indicated nearly identical supervision intensity scores for non-CBT-oriented programs ( M = 0.76, SD = 0.25) and CBT-oriented programs ( M = 0.76, SD = 0.22). For supervision training rigor, an independent-samples t test also indicated no significant difference, t (64) = -0.74, p = .465, Cohen’s d = -0.20. The assumption of homogeneity of variances was met, F (1, 64) = 3.02, p = .087. Descriptive statistics showed somewhat higher rigor scores among CBT-oriented programs ( M = 4.05, SD = 0.91) relative to non-CBT-oriented programs ( M = 3.83, SD = 1.19), although this difference was small and not statistically significant. Table 7 Exploratory t test comparing supervision intensity across CBT-oriented and non-CBT-oriented programs.Independent Samples T-Test 95% CI for Cohen's d t df p Cohen's d SE Cohen's d Lower Upper Supervision Intensity 0.025 64 .980 0.007 0.272 -0.526 0.540 Note. Student's t-test. Assumption Checks Test of Equality of Variances (Levene's) F df 1 df 2 p Supervision Intensity 0.018 1 64 .894 Descriptives Table 8 Exploratory t test comparing supervision training rigor across CBT-oriented and non-CBT-oriented programs. Assumption Checks Group Descriptives Group N Mean SD SE Coefficient of variation Supervision Intensity 0 47 0.757 0.246 0.036 0.324 1 19 0.756 0.223 0.051 0.295 Independent Samples T-Test 95% CI for Cohen's d t df p Cohen's d SE Cohen's d Lower Upper Supervision Rigor Index -0.735 64 .465 -0.200 0.273 -0.733 0.335 Note. Student's t-test. Test of Equality of Variances (Levene's) F df 1 df 2 p Supervision Rigor Index 3.023 1 64 .087 Descriptives Group Descriptives Group N Mean SD SE Coefficient of variation Supervision Rigor Index 0 47 3.830 1.185 0.173 0.310 1 19 4.053 0.911 0.209 0.225 Discussion The purpose of the current study was to examine how supervision training is operationalized within APA-accredited health service psychology doctoral programs and to evaluate the extent to which conceptual and procedural components are integrated into supervision curricula. Using a systematic content analysis of supervision course syllabi, the study further examined whether supervision training rigor and supervision intensity differed across clinical, school, and counseling psychology programs, as well as clinical orientation. Overall, findings indicated that supervision training across program types demonstrates both notable strengths and meaningful gaps, with training appearing structured and comprehensive in many respects while also revealing areas requiring further development. Consistent with prior research highlighting variability in supervision training (Falender, 2018; Newman, 2018), nearly all programs required a formal supervision course and incorporated competency evaluation and remediation procedures. Importantly, contrary to concerns that supervision training remains largely didactic, the majority of programs included experiential training components, with over half employing multiple experiential methods such as role-play, simulation, and meta-supervision opportunities. In addition, more than half of programs emphasized procedural skills, either exclusively or in combination with conceptual training. Notably, this finding directly contrasts with the study’s original hypothesis, which anticipated that supervision training would be primarily conceptual in nature. Instead, results indicated that training was predominantly procedural, with 52.2% of programs emphasizing procedural-only approaches. This disconfirmed hypothesis warrants careful consideration. One possible explanation is that training programs may have responded to longstanding critiques in the literature emphasizing the need for more applied, competency-based supervision training, thereby increasing the integration of experiential and procedural elements. Alternatively, the use of syllabi as the primary data source may introduce a documentation bias, as procedural components may be more explicitly outlined than conceptual or discussion-based content. As such, the observed emphasis on procedural training may reflect both genuine curricular shifts and the ways in which training activities are documented in course materials. From an RE-CBT perspective, this procedural emphasis is consistent with supervision models that prioritize active learning, behavioral rehearsal, feedback, and observable competence, suggesting alignment with CBT- and REBT-informed approaches even when not explicitly labeled as such. These findings suggest that many programs are actively addressing APA supervision competency expectations by incorporating applied supervisory skill development into their curricula. Given calls to broaden supervision models beyond traditional formats to better capture contextual and population-specific factors (Riva & Smith, 2024 ), these findings highlight an important area for continued curricular development. A particularly relevant finding is that 28.4% of programs explicitly identified CBT as a supervision orientation, indicating that CBT is a visible framework within a notable subset of supervision curricula, whereas REBT was rarely named explicitly and may be reflected more implicitly. Despite these strengths, the content analysis revealed notable gaps in population-specific supervision training. While all programs included multicultural supervision content, supervision focused on adult populations was substantially more common than supervision explicitly addressing work with children and adolescents. This imbalance is noteworthy given the prevalence of youth and family cases across clinical and school-based training contexts (Accurso et al., 2011 ), and it suggests that experiential supervision opportunities may not consistently target the developmental and systemic complexities associated with working with children and parents. Additionally, the relatively low frequency of in vivo supervision opportunities (7.5%) further highlights a gap in opportunities for direct, applied supervisory practice in real-world contexts. Analyses examining differences across program types indicated that neither supervision training rigor nor supervision intensity differed significantly among clinical, school, and counseling psychology programs, nor clinical orientation. These null findings suggest a relative consistency in supervision training expectations and structure across disciplines, despite differences in applied training contexts and professional roles. Rather than reflecting a lack of specialization, this pattern may indicate shared accreditation standards and common competency benchmarks guiding supervision training across health service psychology programs. This convergence is consistent with earlier findings indicating substantial overlap in supervision training practices across clinical, counseling, and school psychology programs (Romans et al., 1995 ). Implications for Supervision Training and Curricular Improvement The findings of the current study point to several opportunities for curricular refinement. Although experiential training is widely present, greater intentionality in the design and sequencing of experiential supervision activities may further enhance supervisory competence. Structured supervision-of-supervision models, guided feedback processes, and gradual responsibility for supervisory tasks may help ensure that experiential components translate into skill mastery. Existing models provide guidance for such enhancements. Foxwell et al. ( 2017 ) demonstrated that peer mentorship programs offer effective, low-risk opportunities for trainees to practice supervision skills while fostering professional identity development. Rønnestad et al. ( 1997 ) emphasized that supervisory competence develops incrementally through experience and reflection, stressing the importance of repeated, scaffolded supervisory practice across training stages. Consistent with reflective supervision frameworks, effective supervision training should also prioritize the development of strong supervisory relationships, collaborative goal setting, ethical skills, and ongoing reflective learning to support supervisee growth (Franklin, 2011 ). From a cognitive-behavioral perspective, Cummings et al. ( 2015 ) highlighted the value of structured supervision processes, such as agenda setting, collaborative problem-solving, and constructive feedback in supporting supervisee development. Similarly, Bearman et al. ( 2020 ) demonstrated that targeted training in CBT supervision can effectively build supervisory competencies when experiential learning is already embedded. Overall, these models suggest that supervision curricula may benefit from incorporating structured, group-based, and developmentally sequenced supervision experiences (Riva & Smith, 2024 ) that explicitly support professional competence and supervisory skill acquisition (Kaufman & Schwartz, 2004 ). Limitations and Future Directions Several limitations of the study warrant consideration. First, the study relied on syllabus content, which may not fully capture how supervision training is implemented in practice. Faculty may implement experiential activities or population-specific supervision experiences that are not explicitly documented in the syllabi. Second, participation was voluntary, and submitted syllabi may not be fully representative of all APA-accredited programs. In particular, the response rate (~ 16%) raises the possibility of non-response bias, as programs with more developed or structured supervision training may have been more likely to share syllabi. Third, the study examined curricular structure rather than direct measures of supervisee competence or client outcomes. Finally, the small effect sizes observed in the ANOVA analyses (partial η² = .01 and .03) suggest that differences across program types were minimal, and the study may have been underpowered to detect subtle group differences. To advance the field, future research should develop and empirically evaluate population-specific experiential supervision training modules, particularly for work with children, adolescents, and families. Longitudinal studies are needed to examine supervisee development and client outcomes associated with different supervision training approaches. Additionally, future research should explore how supervision training can better prepare school psychologists to fulfill supervisory and leadership roles within school systems, where supervision often occurs in complex, multidisciplinary contexts. These results therefore suggest relative consistency in supervision training across program types, while also highlighting the need for future research with larger samples to more definitively evaluate potential group differences. Conclusion Overall, supervision training in APA-accredited programs appears structured, competency-focused, and inclusive of experiential learning, while still showing meaningful gaps in population-specific training. For the RE-CBT supervision literature, these findings suggest that CBT-oriented supervision is explicitly represented in a notable subset of programs, while many others incorporate practices broadly consistent with RE-CBT principles, including procedural emphasis, experiential learning, and competency-based evaluation. Declarations No funding was received to assist with the preparation of this manuscript. The author has no competing interests to declare that are relevant to the content of this article. Author Contribution S.Q. wrote the main manuscript textM.T. reviewed the manuscript References Accurso, E. C., Taylor, R. M., & Garland, A. F. (2011). Evidence-based practices addressed in community-based children's mental health clinical supervision. 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L., Jr., Forrest, L., Illfelder-Kaye, J., Nelson, P. D., … Willmuth, M. E. (2004). Competencies conference: Future directions in education and credentialing in professional psychology. Journal of Clinical Psychology, 60 , 699– 712. https://doi.org/10.1002/jclp.20016 Kaslow, N. J., Pate, W. E., II, & Thorn, B. (2005). Academic and internship directors’ perspectives on practicum experiences: Implications. Professional Psychology: Research and Practice, 36 , 307–317. https://doi.org/10.1037/0735-7028.36.3.307 Kaufman, J., & Schwartz, T. (2004). Models of supervision: Shaping professional identity. The Clinical Supervisor, 22 (1), 143–158. https://doi.org/10.1300/J001v22n01_10 Keenan-Miller, D., & Corbett, H. I. (2015). Metasupervision: Can students be safe and effective supervisors? Training and Education in Professional Psychology, 9 (4), 315-321. https://doi.org/10.1037/tep0000090 Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). 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Training and supervision practices in clinical, counseling, and school psychology programs. Professional Psychology: Research and Practice, 26 (4), 407– 412. https://doi.org/10.1037/0735-7028.26.4.407 Rønnestad, M. H., Orlinsky, D. E., Parks, B. K., & Davis, J. D. (1997). Supervisors of psychotherapy: Mapping experience level and supervisory confidence. European Psychologist, 2 , 191–201. https://doi.org/10.1027/1016-9040.2.3.191 Scott, K. J., Ingram, K. M., Vitanza, S. A., & Smith, N. G. (2000). Training in supervision: A survey of current practices. The Counseling Psychologist, 28 (3), 403– 422. https://doi.org/10.1177/0011000000283007 Simon, D. J., & Swerdlik, M. E. (2022). Supervision in school psychology: The developmental, ecological, problem-solving model (2nd ed.). Routledge. Simon, D. J., Cruise, T. K., Huber, B. J., Swerdlik, M. E., & Newman, D. S. (2014). Supervision in school psychology: The developmental/ecological/problem-solving model. Psychology in the Schools, 51 (6), 636–648. https://doi.org/10.1002/pits.21772 Sterner, W. R. (2009). Influence of the supervisory working alliance on supervisee work satisfaction and work-related stress. Journal of Mental Health Counseling, 31 (3), 249- 263. https://doi.org/10.17744/mehc.31.3.f3544l502401831g Stoltenberg, C. D., & McNeill, B. W. (2010). IDM supervision: An integrative developmental model for supervising counselors and therapists (3rd ed.). Routledge. Watkins, C. E., Jr., Hook, J. N., Owen, J., DeBlaere, C., Davis, D. E., & Van Tongeren, D. R. (2019). Multicultural orientation in psychotherapy supervision: Cultural humility, cultural comfort, and cultural opportunities. American Journal of Psychotherapy, 72 (2), 38– 46. https://doi.org/10.1176/appi.psychotherapy.20180040 Additional Declarations No competing interests reported. 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It has been designated a distinct professional competency requiring specialized education and training since the American Psychological Association (APA) Competencies Conference in 2002 and the National Council of Schools of Professional Psychology Mission Bay Conference (Bourg et al., 1989; Falender et al., 2004; Kaslow et al., 2004). It has also been a requirement for APA-accredited programs since 1996 (APA, 1996). In fact, supervision is the third most common professional activity performed by psychologists, surpassed only by psychotherapy and psychological assessment (Norcross \u0026amp; Rogan, 2013). Defined broadly, supervision involves a collaborative relationship in which a senior professional supports the development of a trainee\u0026rsquo;s clinical skills, ethical decision-making, professional identity, and reflective practice (Falender \u0026amp; Shafranske, 2004).\u0026nbsp;\u003cstrong\u003eWhereas most doctoral training programs for clinical psychology in the United States include content focused on developing student competencies in delivering CBT (Heatherington et al., 2012), supervision training appears substantially less universal and less emphasized in formal curricula.\u003c/strong\u003e More broadly, training literature has identified persistent gaps in preparing psychologists to deliver culturally responsive and competency-based care, underscoring the need for intentional, developmentally informed training practices that extend beyond technical skill acquisition (Benuto et al., 2019).\u003c/p\u003e\n\u003cp\u003eWithin this broader literature, CBT and REBT provide especially relevant frameworks for supervision because they emphasize structured, collaborative, and skill-based learning. CBT supervision has been described as active, agenda-driven, and competency-focused, with attention to case conceptualization, feedback, and behavioral rehearsal (Milne \u0026amp; Reiser, 2017). Similarly, REBT-informed supervision emphasizes identifying beliefs that may interfere with clinical work while promoting flexible thinking and active skill development. Accordingly, RE-CBT offers a useful lens for examining how supervision is operationalized in doctoral training.\u003c/p\u003e\n\u003cp\u003eIn the context of school and clinical psychology, supervision is uniquely complex. Psychologists often operate in interdisciplinary settings, support diverse populations, and navigate complex legal and ethical frameworks related to schools, families, healthcare systems, and broader community contexts. Effective supervision is therefore critical to preparing psychologists to meet these multifaceted demands. Supervision must also reflect the realities of working with children, adolescents, their caregivers, and adults, particularly in cases involving behavioral or emotional disturbances. However, emerging evidence suggests that current supervision training in school and clinical psychology programs may not be adequately preparing future supervisors or clinicians for these specific challenges (Falender, 2018; Newman, 2018). As such, this article reviews APA accreditation requirements related to supervision and synthesizes current empirical research on supervision training in health service psychology, with particular attention to school, clinical, and counseling psychology contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPA Guidelines for Clinical Supervision in Health Service Psychology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe American Psychological Association (APA) recognizes supervision as a\u0026nbsp;foundational\u0026nbsp;and\u0026nbsp;functional\u0026nbsp;competency in its competency benchmarks (APA, 2025; Rodolfa et al., 2005). The APA Guidelines on Supervision outline six domains that define effective, ethical, and competency-based supervision in health service psychology. These domains emphasize the need for supervisors to maintain current clinical and supervisory competence; integrate multicultural humility and responsiveness; build strong supervisory alliances; utilize structured teaching, observation, and feedback strategies; manage problems of professional competence through clear evaluation and remediation processes; as well as uphold ethical, legal, and regulatory responsibilities through clear communication and documentation. Together, the Guidelines provide a blueprint designed to promote high-quality, culturally informed supervision that protects clients and supports supervisee development (APA, 2025).\u003c/p\u003e\n\u003cp\u003eBuilding on this framework, the \u003cem\u003eCompetency Benchmarks\u003c/em\u003e model developed by Fouad and colleagues (2009) proposed a developmental approach such that competence develops progressively and requires intentional training, systematic assessment, and clear performance expectations, suggesting the need for explicit instruction and evaluation of supervision-related competencies within graduate education (Fouad et al., 2009).\u003c/p\u003e\n\u003cp\u003eTogether, the APA Guidelines on Supervision and the Competency Benchmarks model emphasizes supervision as a distinct, competency-based professional practice that requires intentional, developmentally sequenced training and systematic evaluation (APA, 2025; Fouad et al., 2009). These competency-based principles overlap closely with RE-CBT supervision, which emphasizes clear goals, structured teaching, and repeated opportunities to translate knowledge into practice. Examining whether supervision curricula include procedural and skill-based elements is therefore relevant not only to supervision broadly, but also to CBT- and REBT-informed supervision. By articulating clear expectations for supervisor competence, multicultural humility, ethical practice, and assessment across training levels (Stoltenberg \u0026amp; McNeill, 2010; Watkins et al., 2019), these frameworks provide a strong foundation for high-quality supervision in health service psychology. However, the extent to which these standards are consistently reflected in graduate training curricula remains an empirical question, highlighting the importance of examining how supervision is taught and operationalized within APA-accredited programs.\u0026nbsp;\u003cstrong\u003eGiven that practicing supervision requires training in supervision and the assessment of supervision competencies (Bernard \u0026amp; Goodyear, 2014), these frameworks also imply the need for intentional curriculum design that supports competent supervisory practice amongst trainees.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModels of Clinical Supervision in Psychology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupervision practice evolved from an apprenticeship model and psychotherapy theory. It was assumed that clinical knowledge and skills derived from psychotherapy models were directly transferable to supervision practice (Falender \u0026amp; Shafranske, 2004). As supervision has been increasingly recognized as a distinct professional competency (APA, 2025), a number of theoretical models has emerged to guide how supervision is conceptualized, taught, and evaluated. Some of the most prominent models are outlined below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopmental Model.\u0026nbsp;\u003c/strong\u003eDevelopmental models conceptualize supervision as a process that evolves with the supervisee\u0026apos;s professional maturity. One widely cited framework is the\u0026nbsp;Integrated Developmental Model (IDM), which outlines progressive stages of competence across clinical, conceptual, and interpersonal domains (Stoltenberg \u0026amp; McNeill, 2010). Supervision within this model is tailored to the supervisee\u0026rsquo;s level, with increasing autonomy and complexity over time. The IDM further emphasizes that supervisees progress through developmental levels characterized by changes in self-awareness, motivation, and perceived independence. As such, supervisors are required to adjust their supervisory responsibilities by incorporating structured feedback, goal setting, and changing roles to support growth (Stoltenberg \u0026amp; McNeill, 2010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscrimination Model.\u0026nbsp;\u003c/strong\u003eThe\u0026nbsp;Discrimination Model\u0026nbsp;(Bernard, 1979) is one of the most widely used supervision models in training programs. It encourages supervisors to shift roles, as teacher, counselor, or consultant, based on the supervisee\u0026rsquo;s needs, as well as specific skill related to intervention and case conceptualization. This model has been favored for its flexibility and applicability to diverse training contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopmental, Ecological, Problem-Solving (DEP) Model.\u0026nbsp;\u003c/strong\u003eWidely used in school psychology, the DEP model tailored supervision training to supervisee readiness as it relates to schools, families, interdisciplinary teams, and organizational systems. Emphasizing collaborative problem-solving, reflective practice, and skill scaffolding, the DEP model supports increasing autonomy while maintaining client care and system-level effectiveness (Simon et al., 2014; Simon \u0026amp; Swerdlik, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompetency-Based Models.\u0026nbsp;\u003c/strong\u003eThe\u0026nbsp;Competency-Based Supervision Model\u0026nbsp;(Falender \u0026amp; Shafranske, 2004, 2007) aligns closely with APA\u0026rsquo;s emphasis on evidence-based practice and guidelines. This model encourages supervisors to explicitly teach and evaluate competencies across areas such as ethics, diversity, legal issues, and assessment. It serves as a bridge between conceptual knowledge and procedural application, a particularly important feature given the gaps in experiential training identified in many supervision courses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Vertical Supervision Model.\u0026nbsp;\u003c/strong\u003eVertical supervision involves a hierarchical training system in which more experienced clinicians supervise less experienced trainees. Meta-supervision may be included in this model, whereby supervisors evaluate the supervisee\u0026rsquo;s development and competence as a supervisor. Research suggests this model can enhance reflective practice and self-efficacy when supervisors model reflection and support self-assessment (Curtis et al., 2016). Keenan-Miller and Corbett (2015) found that advanced doctoral students were safe and effective supervisors when supported through meta-supervision (Keenan-Miller \u0026amp; Corbett, 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough these models provide important frameworks for supervision practice, other approaches rooted in psychotherapy theory, such as cognitive-behavioral supervision incorporating experiential rehearsal and role-play (Milne \u0026amp; Reiser, 2017), as well as cognitive, psychodynamic, and family therapy\u0026ndash;based supervision models (Beck et al., 2008), also remain influential. \u003cstrong\u003eCBT- and REBT-informed supervision models are highly relevant to contemporary training, as they emphasize structured, collaborative, and feedback-oriented approaches, including case conceptualization, behavioral rehearsal, and the examination of therapist beliefs to promote cognitive flexibility and skill development (Milne \u0026amp; Reiser, 2017).\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHowever, psychotherapy-derived models alone may insufficiently address core supervisory processes, including continuous evaluation of supervisee development, the supervisory relationship, multicultural training, ethical and legal responsibilities, as well as supervisee competence (Falender, 2018). \u003cstrong\u003eThus, CBT- and REBT-informed approaches are especially useful for evaluating whether supervision curricula extend beyond conceptual instruction to include the experiential and procedural components central to effective supervision training.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAlthough these models inform how supervision is structured, taught, and evaluated in graduate programs, the extent to which they are implemented consistently across curricula remains in question.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical Research on Supervision Training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with APA accreditation requirements, graduate programs in health service psychology are expected to provide training in supervision, including both conceptual knowledge and applied skills (APA, 2025). However, recent critiques have highlighted significant variability and limitations in how supervision training is implemented (Falender, 2018; Newman, 2018). Although empirical research indicates that most APA-accredited doctoral programs include some didactic instruction in supervision, many programs emphasize theoretical content while offering limited experiential opportunities for students to apply supervision principles in practice (Kaslow et al., 2007; Falender \u0026amp; Shafranske, 2004). This typically includes assigned readings and/or videos, and theoretical content focused on supervision models and ethics. However, the\u0026nbsp;depth\u0026nbsp;and\u0026nbsp;consistency\u0026nbsp;of this instruction vary considerably between programs. In a national survey of training directors, Newman et al., (2018) found that while most programs report offering supervision training, only a small percentage provide structured curricula with both conceptual and applied components. The majority of programs rely on lecture-based instruction, with minimal opportunities for supervisees to practice supervision skills in real or simulated environments.\u0026nbsp;This pattern reflects earlier findings indicating that formal supervision training has historically been inconsistently embedded in graduate education. Further, surveys of training directors conducted in 2000 found that fewer than one-third of training programs included supervision training at that time and surveys on supervision practice by training directors have not been conducted since then (Scott et al., 2000).\u0026nbsp;\u003cstrong\u003eAdditional evidence highlights that relatively few practicing supervisors report having received formal supervision coursework, with fewer than 20% endorsing such training in one national sample (Peake et al., 2002).\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSimilarly, Kaslow and colleagues (2005) found\u0026nbsp;\u003c/strong\u003ethat many training directors in U.S. psychology graduate programs did not view supervision training as a top priority compared to other areas of training, suggesting a gap between how important supervision is to the profession and how much emphasis it actually receives in training programs.\u003c/p\u003e\n\u003cp\u003eThe lack of procedural or experiential training in supervision is a recurring concern in the literature. Falender (2018) has argued that \u0026ldquo;inadequate attention\u0026rdquo; is given to training that allows students to actively engage in supervision tasks, such as conducting mock supervision sessions, engaging in live supervision, or receiving feedback on their supervisory practice. This gap is particularly problematic given that supervision is a learned skill that cannot be fully acquired through reading or discussion alone. In the few studies that have examined applied supervision training, findings suggest that programs incorporating experiential components see improved supervisee confidence and competence (Sterner, 2009; Ladany et al., 2001). Such components are essential in preparing future psychologists for leadership, consultation, and training roles; responsibilities that school psychologists often assume in their professional practice. Consistent with these concerns, Crook-Lyon and colleagues (2008)\u0026nbsp;\u003cstrong\u003ereported in a national survey of 233 U.S. predoctoral interns that only 39% had completed a graduate-level course in supervision.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAlthough these foundational studies provide important historical context, many were conducted over two decades ago, and supervision training practices may have evolved in response to updated APA guidelines, competency-based frameworks, and increased emphasis on experiential training; however, the extent of such changes remains unclear, underscoring the need for contemporary empirical examination.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has also highlighted a\u0026nbsp;lack of specificity\u0026nbsp;in supervision training related to population diversity. Few programs provide targeted supervision training for work involving\u0026nbsp;children, adolescents, and families, despite the high prevalence of such cases in school and clinical settings (Accurso et al., 2011). This gap is particularly concerning given the diverse populations served by psychologists. For instance, child and adolescent clients, especially those with disruptive behavior disorders, represent one of the most frequently encountered groups in clinical settings (Accurso et al., 2011). Supervising clinical work with these populations requires specialized knowledge and competencies, both for the supervisee and the supervisor. A generalized or \u0026ldquo;one size fits all\u0026rdquo; approach to supervision training may inadequately prepare graduate students for these challenges. Therefore, examining the current state of supervision training, including its scope, depth, and specificity is essential to informing curriculum development and improving service quality and trainee development.\u003c/p\u003e\n\u003cp\u003eThe literature to date highlights several important conclusions regarding supervision in psychology training. Supervision is recognized as a critical competency, yet its instruction is often inconsistent across programs. While conceptual instruction in supervision is more common, experiential training is less frequently provided, despite evidence that applied practice enhances supervisory effectiveness. Although theoretical models such as the Integrated Developmental Model (IDM) and the Discrimination Model are referenced in training, their practical application remains unknown. Moreover, population-specific supervision training, particularly for children, adolescents, and families is largely absent or not explicitly addressed. These gaps are especially pronounced in school psychology, a field grounded in applied, systems-level practice.\u0026nbsp;Supervisor perspectives further suggest that these training gaps may have lasting effects. In a national study of supervisors, Nelson and Graves (2011) found that many supervisors perceived advanced trainees as underprepared in core professional competencies, indicating a mismatch between graduate training experiences and the level of competence expected for independent practice.\u0026nbsp;\u003cstrong\u003eRelatedly, Furr and Brown-Rice (2016) found that doctoral students demonstrated limited knowledge of educators\u0026rsquo; problems of professional competency, raising concerns about trainees\u0026rsquo; preparedness to identify, evaluate, and respond to competency concerns, skills that are central to effective supervision.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the field has made strides in conceptualizing supervision as a measurable and teachable competency, much of the existing research is\u0026nbsp;descriptive\u0026nbsp;or\u0026nbsp;survey-based, relying on self-report data from faculty or training directors. There is a lack of longitudinal,\u0026nbsp;experimental, or\u0026nbsp;mixed-methods studies\u0026nbsp;examining the impact of different training approaches on supervisee outcomes. Further, the literature is heavily concentrated in clinical and counseling psychology, with limited focus on supervision training within\u0026nbsp;school psychology\u0026nbsp;programs. This is a significant oversight given the unique demands of school-based practice and the diversity of clients served.\u003c/p\u003e\n\u003cp\u003eDespite the clarity of the newly revised APA guidelines, little is known about how APA-accredited health service psychology programs actually teach supervision to graduate students who will become future psychologists. This study is needed for several key reasons. First, supervision is a core professional competency of mental health training; therefore, understanding how programs prepare trainees for supervisory roles is essential. Second, because supervisors carry the highest duty to protect clients and the public, evaluating supervision training is critical for ensuring client welfare. Third, the Guidelines are aspirational rather than prescriptive, creating significant variability in how programs interpret and implement supervision training. As such, systematic examination can identify inconsistencies, gaps, and areas of strength across programs. Fourth, the Guidelines highlight the need for supervision that integrates a multicultural orientation, yet it remains unclear how consistently programs embed cultural humility and opportunities into supervision curricula. Fifth, the Guidelines call for research linking supervision practices to client outcomes and understanding how supervision is taught is a necessary foundation for such investigations. Sixth, existing literature suggests many licensed psychologists feel underprepared to supervise, strengthening the need to examine whether supervision training at the graduate level is sufficient and evidence based. Seventh, the Guidelines are intended to be a living document, updated based on emerging evidence. Comprehensive data on training practices across program types are essential to inform future revisions. Finally, strengthening supervision training is vital to the future of the profession, as high-quality supervision is directly tied to therapist competence, ethical practice, and client care.\u003c/p\u003e\n\u003cp\u003eBy evaluating how APA-accredited health service psychology programs train future psychologists in supervision and examining whether supervision training characteristics differ across clinical, school, and counseling psychology programs, this study directly responds to the priorities articulated in the Guidelines and contributes to improving training quality, enhancing public protection, and advancing the science and practice of clinical supervision.\u0026nbsp;These concerns are especially salient from a CBT/REBT perspective, as RE-CBT supervision emphasizes modeling, feedback, guided discovery, and rehearsal rather than theoretical discussion alone (Milne \u0026amp; Reiser, 2017). Thus, the extent to which doctoral supervision curricula include structured experiential training is highly relevant to their alignment with RE-CBT-informed supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Current Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study used a content analysis of supervision syllabi from APA-accredited doctoral programs across the United States to assess the degree to which conceptual and procedural components are integrated into supervision training. Specifically, the study examined supervision training in APA-accredited health service psychology programs by investigating the extent to which curricula include conceptual (didactic/theoretical) and procedural (experiential/practical) components.\u0026nbsp;\u003cstrong\u003eThe first research question examined how supervision training is operationalized and evaluated in APA-accredited programs overall, including the specific supervision training features present across programs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, the study examined whether supervision training characteristics differ systematically across program emphases, clinical psychology, school psychology, and counseling psychology. One-way analyses of variance (ANOVAs) were used to examine differences in supervision training rigor and intensity across program types. \u003cstrong\u003eIn addition, exploratory independent samples t-tests were conducted to examine whether supervision training characteristics differed between CBT-oriented and non-CBT-oriented programs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was hypothesized that most APA-accredited programs would emphasize conceptual supervision training, including readings, theoretical frameworks, and discussions of supervisory models, whereas fewer programs would include procedural or experiential components such as in vivo supervision, role plays, or applied supervision exercises.\u0026nbsp;\u003cstrong\u003eGiven inconsistencies in the existing literature, it was further hypothesized that supervision training rigor and supervision intensity\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003emay\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003ediffer across program types, operationalized as indices reflecting the integration and depth of supervision training. Exploratory analyses were expected to provide preliminary insight into whether CBT-oriented programs demonstrate greater procedural emphasis or structured training features compared to non-CBT programs.\u003c/strong\u003e By systematically analyzing supervision syllabi and comparing training characteristics across program types, this study aimed to provide data-driven insights into current supervision training practices and inform evidence-based recommendations for curricular improvement. More specifically, the study offers preliminary insight into whether contemporary supervision curricula reflect features consistent with RE-CBT supervision models, particularly structure, procedural emphasis, and competency-based training.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eThis study utilized a systematic content analysis methodology to examine the extent and nature of supervision training within APA-accredited health service psychology doctoral programs. The primary data source consisted of course syllabi from doctoral-level courses that explicitly addressed clinical supervision. The study sought to evaluate the balance between conceptual and procedural supervision training, as well as the extent to which supervision training incorporated applied competency evaluation, remediation processes, multicultural considerations, and population-specific supervision content. These variables were subsequently operationalized into quantitative indices of supervision training rigor and intensity. Content analysis is an appropriate methodology for systematically identifying patterns, themes, and frequencies of content elements in textual documents such as course syllabi (Krippendorff, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInitial search.\u003c/b\u003e An extensive search of APA-accredited health service psychology programs was conducted using the official APA Accreditation website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://accreditation.apa.org/accredited-programs\u003c/span\u003e\u003cspan address=\"https://accreditation.apa.org/accredited-programs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The search began by selecting \u0026ldquo;Start Your Search\u0026rdquo; and applying the following criteria: doctoral-level programs, all U.S. states, selected substantive areas including Clinical Psychology, Counseling Psychology, and School Psychology, and all doctoral degrees (PhD, PsyD). This strategy was designed to identify the full population of APA-accredited health service psychology doctoral programs and to capture variability in supervision training practices across program types and training models. There was a total of 415 programs identified and contacted. 67 programs provided their supervision course syllabi (n\u0026thinsp;\u0026asymp;\u0026thinsp;16%). The response rate of approximately 16% is relatively low and represents a potential source of non-response bias. Programs that elected to share syllabi may differ systematically from those that did not, such as having more developed or structured supervision training. As a result, findings may overrepresent programs with stronger or more formalized supervision curricula and should be interpreted with this context in mind.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary search.\u003c/b\u003e Following the initial identification of APA-accredited programs, a secondary search was conducted to obtain supervision-related syllabi. Using the list of eligible programs, each program\u0026rsquo;s official university website was reviewed to identify key personnel, including program directors, directors of clinical training, department chairs, or faculty members teaching supervision courses. These individuals were contacted via email to explain the purpose of the study and request copies of supervision-focused syllabi, including didactic supervision courses and supervision practica. When initial contact attempts were unsuccessful, follow-up emails were sent to maximize response rates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eThe author and a team of graduate research assistants (RAs) initially reviewed each program listing and associated course materials to determine eligibility based on the following criteria. Courses were included if they (a) were primarily focused on supervision rather than general clinical practice, ethics, or other topics, and (b) were offered within a doctoral-level APA-accredited program. Courses were excluded if they did not explicitly address supervision content or were part of non-APA-accredited programs, undergraduate programs, master\u0026rsquo;s-level or certificate-only programs. Additionally, courses were excluded if syllabi or sufficient course documentation could not be obtained after reasonable efforts to contact program personnel. This approach ensures that only doctoral-level, supervision-specific coursework was evaluated.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation Criteria\u003c/h2\u003e \u003cp\u003eA structured coding framework was developed to extract relevant information from each syllabus. The framework was informed by the APA Guidelines for Clinical Supervision in Health Service Psychology (APA, 2025), competency-based supervision literature (Falender \u0026amp; Shafranske, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and a pilot review of a subset of syllabi. After finalizing the sample, each syllabus was coded on a set of structured variables capturing course structure, instructional features, experiential training components, and competency evaluation practices.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCourse Structure.\u003c/b\u003e Four variables described the structure and instructional scope of each course: (1) total number of instructional weeks in the course (excluding holidays or weeks with no class), (2) number of weeks explicitly devoted to supervision instruction, (3) number of required readings, and (4) number of required videos or multimedia learning components. The number of required readings was coded as the total count of all required instructional materials explicitly listed on the syllabus, including textbooks and peer-reviewed journal articles. Each required textbook chapter and each required article was coded as one reading. Recommended or optional materials were excluded from this count.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupervision Intensity Index.\u003c/b\u003e A composite supervision intensity index was calculated to represent the proportion of course time devoted to supervision content. This index was computed as the ratio of supervision-specific instructional weeks to total course weeks (Supervision Weeks\u0026thinsp;\u0026divide;\u0026thinsp;Total Weeks), yielding a continuous value ranging from 0 to 1, with higher values indicating greater emphasis on supervision within the course.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstructional and Training Features.\u003c/b\u003e Several variables assessed key instructional features of supervision training. Meta-supervision was coded as present when the syllabus included structured opportunities for supervision of supervision, including faculty-led meta-supervision or organized peer supervision activities. Clinical focus was coded as present when supervision training was framed around clinical service delivery, including psychotherapy or related health service psychology practices. Supervision model was coded based on the primary clinical orientation used to frame supervision practice. Courses that emphasized supervision across multiple psychotherapy orientations (e.g., CBT, psychodynamic, REBT) or explicitly allowed students to integrate multiple approaches were coded as integrative. CBT or psychodynamic supervision was coded only when the syllabus explicitly privileged that orientation as the primary supervisory framework.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCompetency Evaluation.\u003c/b\u003e The remaining variables evaluated how supervisory competence was assessed and remediated. Competency evaluation was coded as present when the syllabus included explicit evaluation of supervisory competence. Evaluation method was coded based on whether competence was assessed via written methods (e.g., exams, papers), performance-based methods (e.g., case presentations), or both. Remediation was coded as present when the syllabus specified any academic or programmatic action taken if a student failed the course or failed to meet competency standards, including course repetition, delayed progression, remediation, or dismissal.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupervision Training Rigor Index.\u003c/b\u003e A composite supervision training rigor index was created by summing key instructional and evaluation features, including the presence of meta-supervision, clinical focus, competency evaluation, experiential training, multicultural supervision, and remediation procedures (each coded as 0\u0026thinsp;=\u0026thinsp;absent, 1\u0026thinsp;=\u0026thinsp;present). Scores were summed to yield a total rigor score, with higher values reflecting greater integration of conceptual, procedural, and competency-based training components. The resulting scale ranged from 0 to the total number of included features, representing increasing levels of training rigor.\u003c/p\u003e \u003cp\u003eAll syllabi were coded using this rubric to ensure consistency in data extraction and analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and Analysis\u003c/h3\u003e\n\u003cp\u003eData were extracted from each eligible syllabus and coded on all evaluation variables using Microsoft Excel. The dataset was subsequently imported into JASP for data cleaning, descriptive analyses, and interrater reliability calculations, as well as the computation of composite supervision intensity and supervision training rigor indices.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCoding Approach.\u003c/b\u003e The author and a team of trained RAs coded each syllabus according to the established evaluation criteria. The RAs were all doctoral-level school psychology students trained in systematic coding procedures. Each rater participated in a group training session with the author, an individual training meeting, and received a detailed coding manual outlining the operational definitions of each variable, examples of coding decisions, and decision rules for each variable. During the training phase, all coders jointly coded ten syllabi with the author to establish interrater reliability, aiming for a minimum Cohen\u0026rsquo;s Kappa of 0.80. Once this reliability threshold was reached, the RAs proceeded to code the remaining syllabi. In cases where discrepancies occurred that reduced reliability below this threshold, the raters met with the author to discuss and resolve differences, and the consensus decision was recorded on the master coding sheet. All coding were based on the full content of each syllabus, including course descriptions, objectives, required readings and media, assignments, supervision orientation statements, and competency evaluation procedures. When relevant information was unclear, coders documented the missing data, and clarification was sought from the program contact when feasible.\u003c/p\u003e \u003cp\u003eThroughout the data collection and coding process, the author held regular meetings with the research team to review progress, discuss discrepancies, and ensure consistency across raters. Consensus meetings were used to refine definitions and resolve ambiguities, enhancing the reliability and validity of the dataset. The finalized coded dataset was analyzed descriptively to identify trends and patterns across programs in the inclusion and structure of supervision training. To examine whether supervision training intensity and supervision training rigor differed by discipline, one-way analyses of variance (ANOVAs) were conducted comparing clinical, school, and counseling psychology programs on the composite indices. Additionally, independent samples t-tests were conducted as exploratory analyses to examine differences in supervision training intensity and rigor between programs that explicitly endorsed a CBT orientation and those that did not.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCohen\u0026rsquo;s Kappa for Inter-rater Reliability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohen\u0026rsquo;s Kappa\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse Required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeta-Supervision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompetency Eval Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiential Training Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiential Training Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedural Emphasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticultural Supervision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemediation Outlined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCohen\u0026rsquo;s kappa values demonstrated overall strong interrater reliability across coded variables (κ\u0026thinsp;=\u0026thinsp;.66\u0026ndash;1.00). A few variables showed lower agreement, most notably remediation outlined (κ\u0026thinsp;=\u0026thinsp;.49) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the majority of the 67 APA-accredited health service psychology programs were clinical psychology programs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38, 56.7%), followed by school (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, 22.4%) and counseling (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13, 19.4%) programs, with few combined programs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 1.5%). Most programs awarded a PhD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55, 82.1%), compared to a smaller proportion offering a PsyD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12, 17.9%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProgram Type and Degree Frequencies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgram Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounseling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsyD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDescriptive statistics for supervision training variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Supervision intensity across programs was relatively high on average (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24). Programs included a mean of 12.34 total weeks (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.44) of supervision-related content, with an average of 9.18 weeks (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.79) specifically devoted to supervision. With respect to didactic components, programs assigned an average of 24.60 readings (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.02) and 1.03 videos (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.47).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for Supervision Training Variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupervision Intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNum. Readings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNum. Videos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCourse Requirements and Orientation.\u003c/b\u003e Nearly all programs required a formal supervision course (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, 98.5%). A clinical focus was identified in the majority of programs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51, 76.1%); however, specific theoretical orientations were often not specified (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35, 52.2%). When identified, cognitive behavioral therapy was the most frequently endorsed orientation (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19, 28.4%), followed by integrative approaches (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11, 16.4%) and psychodynamic orientations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, 3.0%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation and Accountability.\u003c/b\u003e Competency-based evaluation was present in nearly all programs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, 98.5%). Most programs employed multiple evaluation methods (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45, 67.2%), whereas fewer relied solely on written examinations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20, 29.9%) or case presentations alone (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 1.5%). Additionally, the vast majority of programs explicitly outlined remediation procedures (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, 98.5%). Approximately half of programs also incorporated meta-supervision components (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, 49.3%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExperiential and Procedural Training.\u003c/b\u003e Experiential training components were included in most programs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;62, 92.5%). Of these, more than half utilized multiple experiential methods (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37, 55.2%), while others relied solely on role-play or simulation (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20, 29.9%) or live/in vivo supervision alone (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5, 7.5%). A small subset did not specify experiential methods (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5, 7.5%). In terms of training emphasis, most programs prioritized procedural skills (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35, 52.2%), with fewer programs adopting a mixed conceptual\u0026ndash;procedural approach (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29, 43.3%) or a conceptual-only emphasis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, 4.5%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiversity and Population Focus.\u003c/b\u003e All programs included content related to multicultural supervision (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67, 100%). However, a greater proportion of programs emphasized adult populations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46, 68.7%) compared to child and adolescent populations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, 31.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequencies and Percentages of Supervision Course Features and Training Components.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCourse Required\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRequired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Focus \u0026amp; Orientation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eClinical focus present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePsychodynamic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIntegrative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNot Specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvaluation \u0026amp; Accountability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCompetency Evaluation Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWritten/Exam Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCase Presentation Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRemediation Procedures Outlined\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeta-Supervision Included\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExperiential \u0026amp; Procedural Training\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eExperiential Training Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRole Play/Simulation Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLive/In Vivo Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMultiple Methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNot Specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural Emphasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eConceptual Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eProcedural Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversity \u0026amp; Population Focus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMulticultural Supervision Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAdult Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChild/Adolescent Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, one-way analyses of variance (ANOVAs) were conducted to examine whether supervision intensity and supervision training rigor differed across clinical, school, and counseling psychology programs. For both analyses, the assumption of homogeneity of variances was met (supervision intensity: \u003cem\u003eF\u003c/em\u003e (2, 63)\u0026thinsp;=\u0026thinsp;0.92, \u003cem\u003ep\u003c/em\u003e = .403; supervision rigor: \u003cem\u003eF\u003c/em\u003e (2, 63)\u0026thinsp;=\u0026thinsp;1.55, \u003cem\u003ep\u003c/em\u003e = .220). Results indicated that neither supervision intensity, \u003cem\u003eF\u003c/em\u003e (2, 63)\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003ep\u003c/em\u003e = .705, partial η\u0026sup2; = .01, nor supervision training rigor, \u003cem\u003eF\u003c/em\u003e (2, 63)\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003ep\u003c/em\u003e = .411, partial η\u0026sup2; = .03, differed significantly by program type. Descriptive statistics showed comparable levels of supervision intensity and rigor across program types. Although these findings were not statistically significant, the small effect sizes (partial η\u0026sup2; = .01 and .03) suggest minimal practical differences across program types. However, it is important to consider that the present study may have been underpowered to detect moderate effects, given the relatively small and uneven group sizes (n\u0026thinsp;=\u0026thinsp;13\u0026ndash;38 per group). As such, the absence of statistically significant differences should be interpreted with caution, as meaningful differences may not have been detectable with the available sample size.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" 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\" width=\"724\" height=\"619\"\u003e\u003c/p\u003e\u003cp\u003eAs depicted in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, exploratory analyses were conducted to examine whether CBT-oriented programs differed from non-CBT-oriented programs on supervision intensity and supervision training rigor. For supervision intensity, an independent-samples \u003cem\u003et\u003c/em\u003e test indicated no significant difference between CBT-oriented and non-CBT-oriented programs, \u003cem\u003et\u003c/em\u003e(64)\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e = .980, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01. The assumption of homogeneity of variances was met, \u003cem\u003eF\u003c/em\u003e(1, 64)\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e = .894. Descriptive statistics indicated nearly identical supervision intensity scores for non-CBT-oriented programs (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25) and CBT-oriented programs (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22). For supervision training rigor, an independent-samples \u003cem\u003et\u003c/em\u003e test also indicated no significant difference, \u003cem\u003et\u003c/em\u003e(64) = -0.74, \u003cem\u003ep\u003c/em\u003e = .465, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = -0.20. The assumption of homogeneity of variances was met, \u003cem\u003eF\u003c/em\u003e(1, 64)\u0026thinsp;=\u0026thinsp;3.02, \u003cem\u003ep\u003c/em\u003e = .087. Descriptive statistics showed somewhat higher rigor scores among CBT-oriented programs (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91) relative to non-CBT-oriented programs (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.83, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.19), although this difference was small and not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory t test comparing supervision intensity across CBT-oriented and non-CBT-oriented programs.Independent Samples T-Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI for Cohen's d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCohen's d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE Cohen's d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u0026nbsp;Student's t-test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssumption Checks\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTest of Equality of Variances (Levene's)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eDescriptives\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory t test comparing supervision training rigor across CBT-oriented and non-CBT-oriented programs. \u003cb\u003eAssumption Checks\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroup Descriptives\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient of variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndependent Samples T-Test\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI for Cohen's d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCohen's d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE Cohen's d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Rigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u0026nbsp;Student's t-test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTest of Equality of Variances (Levene's)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Rigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eDescriptives\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabi\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroup Descriptives\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient of variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervision Rigor Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of the current study was to examine how supervision training is operationalized within APA-accredited health service psychology doctoral programs and to evaluate the extent to which conceptual and procedural components are integrated into supervision curricula. Using a systematic content analysis of supervision course syllabi, the study further examined whether supervision training rigor and supervision intensity differed across clinical, school, and counseling psychology programs, as well as clinical orientation. Overall, findings indicated that supervision training across program types demonstrates both notable strengths and meaningful gaps, with training appearing structured and comprehensive in many respects while also revealing areas requiring further development.\u003c/p\u003e \u003cp\u003eConsistent with prior research highlighting variability in supervision training (Falender, 2018; Newman, 2018), nearly all programs required a formal supervision course and incorporated competency evaluation and remediation procedures. Importantly, contrary to concerns that supervision training remains largely didactic, the majority of programs included experiential training components, with over half employing multiple experiential methods such as role-play, simulation, and meta-supervision opportunities. In addition, more than half of programs emphasized procedural skills, either exclusively or in combination with conceptual training. Notably, this finding directly contrasts with the study\u0026rsquo;s original hypothesis, which anticipated that supervision training would be primarily conceptual in nature. Instead, results indicated that training was predominantly procedural, with 52.2% of programs emphasizing procedural-only approaches. This disconfirmed hypothesis warrants careful consideration. One possible explanation is that training programs may have responded to longstanding critiques in the literature emphasizing the need for more applied, competency-based supervision training, thereby increasing the integration of experiential and procedural elements. Alternatively, the use of syllabi as the primary data source may introduce a documentation bias, as procedural components may be more explicitly outlined than conceptual or discussion-based content. As such, the observed emphasis on procedural training may reflect both genuine curricular shifts and the ways in which training activities are documented in course materials. From an RE-CBT perspective, this procedural emphasis is consistent with supervision models that prioritize active learning, behavioral rehearsal, feedback, and observable competence, suggesting alignment with CBT- and REBT-informed approaches even when not explicitly labeled as such. These findings suggest that many programs are actively addressing APA supervision competency expectations by incorporating applied supervisory skill development into their curricula. Given calls to broaden supervision models beyond traditional formats to better capture contextual and population-specific factors (Riva \u0026amp; Smith, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), these findings highlight an important area for continued curricular development. A particularly relevant finding is that 28.4% of programs explicitly identified CBT as a supervision orientation, indicating that CBT is a visible framework within a notable subset of supervision curricula, whereas REBT was rarely named explicitly and may be reflected more implicitly.\u003c/p\u003e \u003cp\u003eDespite these strengths, the content analysis revealed notable gaps in population-specific supervision training. While all programs included multicultural supervision content, supervision focused on adult populations was substantially more common than supervision explicitly addressing work with children and adolescents. This imbalance is noteworthy given the prevalence of youth and family cases across clinical and school-based training contexts (Accurso et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and it suggests that experiential supervision opportunities may not consistently target the developmental and systemic complexities associated with working with children and parents. Additionally, the relatively low frequency of in vivo supervision opportunities (7.5%) further highlights a gap in opportunities for direct, applied supervisory practice in real-world contexts.\u003c/p\u003e \u003cp\u003eAnalyses examining differences across program types indicated that neither supervision training rigor nor supervision intensity differed significantly among clinical, school, and counseling psychology programs, nor clinical orientation. These null findings suggest a relative consistency in supervision training expectations and structure across disciplines, despite differences in applied training contexts and professional roles. Rather than reflecting a lack of specialization, this pattern may indicate shared accreditation standards and common competency benchmarks guiding supervision training across health service psychology programs. This convergence is consistent with earlier findings indicating substantial overlap in supervision training practices across clinical, counseling, and school psychology programs (Romans et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Supervision Training and Curricular Improvement\u003c/h2\u003e \u003cp\u003eThe findings of the current study point to several opportunities for curricular refinement. Although experiential training is widely present, greater intentionality in the design and sequencing of experiential supervision activities may further enhance supervisory competence. Structured supervision-of-supervision models, guided feedback processes, and gradual responsibility for supervisory tasks may help ensure that experiential components translate into skill mastery. Existing models provide guidance for such enhancements. Foxwell et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that peer mentorship programs offer effective, low-risk opportunities for trainees to practice supervision skills while fostering professional identity development. R\u0026oslash;nnestad et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) emphasized that supervisory competence develops incrementally through experience and reflection, stressing the importance of repeated, scaffolded supervisory practice across training stages. Consistent with reflective supervision frameworks, effective supervision training should also prioritize the development of strong supervisory relationships, collaborative goal setting, ethical skills, and ongoing reflective learning to support supervisee growth (Franklin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). From a cognitive-behavioral perspective, Cummings et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) highlighted the value of structured supervision processes, such as agenda setting, collaborative problem-solving, and constructive feedback in supporting supervisee development. Similarly, Bearman et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that targeted training in CBT supervision can effectively build supervisory competencies when experiential learning is already embedded. Overall, these models suggest that supervision curricula may benefit from incorporating structured, group-based, and developmentally sequenced supervision experiences (Riva \u0026amp; Smith, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that explicitly support professional competence and supervisory skill acquisition (Kaufman \u0026amp; Schwartz, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations of the study warrant consideration. First, the study relied on syllabus content, which may not fully capture how supervision training is implemented in practice. Faculty may implement experiential activities or population-specific supervision experiences that are not explicitly documented in the syllabi. Second, participation was voluntary, and submitted syllabi may not be fully representative of all APA-accredited programs. In particular, the response rate (~\u0026thinsp;16%) raises the possibility of non-response bias, as programs with more developed or structured supervision training may have been more likely to share syllabi. Third, the study examined curricular structure rather than direct measures of supervisee competence or client outcomes. Finally, the small effect sizes observed in the ANOVA analyses (partial η\u0026sup2; = .01 and .03) suggest that differences across program types were minimal, and the study may have been underpowered to detect subtle group differences.\u003c/p\u003e \u003cp\u003eTo advance the field, future research should develop and empirically evaluate population-specific experiential supervision training modules, particularly for work with children, adolescents, and families. Longitudinal studies are needed to examine supervisee development and client outcomes associated with different supervision training approaches. Additionally, future research should explore how supervision training can better prepare school psychologists to fulfill supervisory and leadership roles within school systems, where supervision often occurs in complex, multidisciplinary contexts. These results therefore suggest relative consistency in supervision training across program types, while also highlighting the need for future research with larger samples to more definitively evaluate potential group differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, supervision training in APA-accredited programs appears structured, competency-focused, and inclusive of experiential learning, while still showing meaningful gaps in population-specific training. For the RE-CBT supervision literature, these findings suggest that CBT-oriented supervision is explicitly represented in a notable subset of programs, while many others incorporate practices broadly consistent with RE-CBT principles, including procedural emphasis, experiential learning, and competency-based evaluation.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eNo funding was received to assist with the preparation of this manuscript. The author has no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.Q. wrote the main manuscript textM.T. reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAccurso, E. C., Taylor, R. M., \u0026amp; Garland, A. F. (2011). 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(2018) \u0026lsquo;What we know and do not know about \u003c/p\u003e\n\u003cp\u003esupervision in school psychology: A systematic mapping and review of the literature \u003c/p\u003e\n\u003cp\u003ebetween 2000 and 2017\u0026rsquo;, \u003cem\u003ePsychology in the Schools\u003c/em\u003e, 56(3), pp. 306\u0026ndash;334. \u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.1002/pits.22182\u003c/p\u003e\n\u003cp\u003eNorcross, J. C., \u0026amp; Rogan, J. D. (2013). Psychologists conducting psychotherapy in 2012: Current \u003c/p\u003e\n\u003cp\u003epractices and historical trends among Division 29 members. \u003cem\u003ePsychotherapy, 50\u003c/em\u003e, 490\u0026ndash;\u003c/p\u003e\n\u003cp\u003e495. https://doi.org/10.1037/a0033512\u003c/p\u003e\n\u003cp\u003ePeake, T. H., Nussbaum, B. D., \u0026amp; Tindell, S. D. (2002). 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E., \u0026amp; Newman, D. S. (2014). Supervision \u003c/p\u003e\n\u003cp\u003ein school psychology: The developmental/ecological/problem-solving model. \u003cem\u003ePsychology \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ein the Schools, 51\u003c/em\u003e(6), 636\u0026ndash;648. https://doi.org/10.1002/pits.21772\u003c/p\u003e\n\u003cp\u003eSterner, W. R. (2009). Influence of the supervisory working alliance on supervisee work \u003c/p\u003e\n\u003cp\u003esatisfaction and work-related stress.\u003cem\u003eJournal of Mental Health Counseling,\u003c/em\u003e\u003cem\u003e31\u003c/em\u003e(3), 249-\u003c/p\u003e\n\u003cp\u003e263. https://doi.org/10.17744/mehc.31.3.f3544l502401831g\u003c/p\u003e\n\u003cp\u003eStoltenberg, C. D., \u0026amp; McNeill, B. W. (2010). \u003cem\u003eIDM supervision: An integrative developmental \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003emodel for supervising counselors and therapists\u003c/em\u003e (3rd ed.). Routledge.\u003c/p\u003e\n\u003cp\u003eWatkins, C. E., Jr., Hook, J. N., Owen, J., DeBlaere, C., Davis, D. E., \u0026amp; Van Tongeren, D. R. \u003c/p\u003e\n\u003cp\u003e(2019). Multicultural orientation in psychotherapy supervision: Cultural humility, cultural \u003c/p\u003e\n\u003cp\u003ecomfort, and cultural opportunities. \u003cem\u003eAmerican Journal of Psychotherapy, 72\u003c/em\u003e(2), 38\u0026ndash;\u003c/p\u003e\n\u003cp\u003e46. https://doi.org/10.1176/appi.psychotherapy.20180040\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"clinical supervision, RE-CBT, APA-accredited, health service psychology","lastPublishedDoi":"10.21203/rs.3.rs-9247979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9247979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClinical supervision is recognized as both a foundational and functional competency in the training of psychologists (Rodolfa et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), with exposure to supervision knowledge emphasized in American Psychological Association (APA) accreditation requirements. However, foundational knowledge does not necessarily translate into effective supervisory skill. Falender (2018) highlighted supervision training as \u0026ldquo;inadequately addressed\u0026rdquo; within many psychology curricula. From a Rational Emotive Cognitive-Behavioral Therapy (RE-CBT) perspective, supervision emphasizes structured, skill-based, and competency-focused training, offering a useful lens for evaluating current practices. The present study examined how competency-based supervision training is integrated within APA-accredited health service psychology doctoral programs, specifically whether training emphasizes conceptual knowledge, procedural application, or both, and whether it varies by program type and population focus.\u003c/p\u003e \u003cp\u003eA systematic content analysis was conducted on supervision course syllabi from APA-accredited doctoral programs across the United States. Of 415 programs contacted, 67 submitted syllabi (\u0026asymp;\u0026thinsp;16%). Syllabi were coded using a structured rubric assessing course features, experiential training, competency evaluation, multicultural content, and population focus. Composite indices of supervision training rigor and intensity were derived to capture the integration of conceptual and procedural elements. Post-hoc independent samples t-tests were conducted to examine differences in supervision training characteristics by clinical orientation.\u003c/p\u003e \u003cp\u003eResults indicated that supervision training was primarily procedural, with most programs including experiential components. Nearly all programs required a supervision course and included competency evaluation; however, fewer syllabi documented in vivo supervision or population-specific applied experiences. No significant differences in rigor or intensity were found across program types, and exploratory independent samples t-tests examining CBT-oriented versus non-CBT-oriented programs did not reveal significant differences in supervision training rigor or intensity. Training focused on children and adolescents was less frequently represented than adult populations. Findings are discussed in relation to APA supervision competencies and RE-CBT-informed supervision practices, with recommendations for strengthening supervision curricula.\u003c/p\u003e","manuscriptTitle":"Supervision Training in Graduate Programs in Psychology: Moving from Conceptual to Procedural","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 14:00:05","doi":"10.21203/rs.3.rs-9247979/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee21f2b6-ca73-45a0-b730-5d00d4960d59","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T22:22:20+00:00","index":11,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T14:00:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 14:00:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9247979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9247979","identity":"rs-9247979","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}