Clinician training, then what? Randomized clinical trial of child STEPs psychotherapy using lower-cost implementation supports with versus without expert consultation.
Bibliographic record
Abstract
OBJECTIVE: Implementation of evidence-based treatments in funded trials is often supported by expert case consultation for clinicians; this may be financially and logistically difficult in clinical practice. Might less costly implementation support produce acceptable treatment fidelity and clinical outcomes? METHOD: To find out, we trained 42 community clinicians from four community clinics in Modular Approach to Therapy for Children (MATCH), then randomly assigned them to receive multiple lower-cost implementation supports (LC) or expert MATCH consultation plus lower-cost supports (CLC). Clinically referred youths (N = 200; ages 7-15 years, M = 10.73; 53.5% male; 32.5% White, 27.5% Black, 24.0% Latinx, 1.0% Asian, 13.5% multiracial, 1.5% other) were randomly assigned to LC (n = 101) or CLC (n = 99) clinicians, and groups were compared on MATCH adherence and competence, as well as on multiple clinical outcomes using standardized measures (e.g., Child Behavior Checklist, Youth Self-Report) and idiographic problem ratings (Top Problems Assessment). RESULTS: Coding of therapy sessions revealed substantial therapist adherence to MATCH in both conditions, with significantly stronger adherence in CLC; however, LC and CLC did not differ significantly in MATCH competence. Trajectories of change on all outcome measures were steep, positive, and highly similar for LC and CLC youths, with no significant differences; a supplemental analysis of posttreatment outcomes also showed similar LC and CLC posttreatment scores, with most LC-CLC differences nonsignificant. CONCLUSIONS: The findings suggest that effective implementation of a complex intervention in clinical practice may be supported by procedures that are less costly and logistically challenging than expert consultation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".