Evidence-Based Psychotherapy Training in Residency and Graduate Programs: A Multidisciplinary Survey
Bibliographic record
Abstract
OBJECTIVE: Training in evidence-based psychotherapies (EBPs) in clinical programs is crucial for increasing their use in practice. Twenty years ago, the National Psychotherapy Training Survey-I (NPTS-I) examined whether U.S. training programs required didactic training and clinical supervision in EBPs. Almost all psychiatry (96%) programs, but only 56% of clinical psychology Ph.D., 33% of clinical psychology Psy.D., and 38% of social work (i.e., M.S.W.) programs, required EBP training. After years of dissemination efforts, the authors reexamined EBP training levels across those disciplines as well as counseling psychology and psychiatric-mental health nurse practitioner (PMHNP) programs by conducting the NPTS-II. METHODS: Half of accredited training programs in each discipline were randomly selected for an online survey of training requirements and electives, whether all trainees receive EBP training, and associated barriers and facilitators. Of 574 invited programs, 253 (44%) program directors completed responses: 48 psychiatry (37%), 44 clinical psychology Ph.D. (53%), 14 clinical psychology Psy.D. (36%), 34 counseling psychology (58%), 38 PMHNP (35%), and 75 M.S.W. (49%) programs. RESULTS: At least 75% of programs across disciplines offered didactic and clinical supervision EBP training. Required training rates were higher in psychiatry (90%) and clinical psychology Ph.D. (81%) programs than in clinical psychology Psy.D. (59%), PMHNP (48%), counseling psychology (28%), and M.S.W. (27%) programs. A similar pattern emerged in estimations of all trainees receiving EBP training. CONCLUSIONS: EBP training requirements held steady in psychiatry and increased in clinical psychology programs. Low EBP training rates persist in other disciplines, possibly contributing to the continuing gap between EBP research efficacy evidence and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".