Therapist adherence/competence and treatment outcome: A meta-analytic review.
Bibliographic record
Abstract
OBJECTIVE: The authors conducted a meta-analytic review of adherence-outcome and competence-outcome findings, and examined plausible moderators of these relations. METHOD: A computerized search of the PsycINFO database was conducted. In addition, the reference sections of all obtained studies were examined for any additional relevant articles or review chapters. The literature search identified 36 studies that met the inclusion criteria. RESULTS: R-type effect size estimates were derived from 32 adherence-outcome and 17 competence-outcome findings. Neither the mean weighted adherence-outcome (r = .02) nor competence-outcome (r = .07) effect size estimates were found to be significantly different from zero. Significant heterogeneity was observed across both the adherence-outcome and competence-outcome effect size estimates, suggesting that the individual studies were not all drawn from the same population. Moderator analyses revealed that larger competence-outcome effect size estimates were associated with studies that either targeted depression or did not control for the influence of the therapeutic alliance. CONCLUSIONS: One explanation for these results is that, among the treatment modalities represented in this review, therapist adherence and competence play little role in determining symptom change. However, given the significant heterogeneity observed across findings, mean effect sizes must be interpreted with caution. Factors that may account for the nonsignificant adherence-outcome and competence-outcome findings reported within many of the studies reviewed are addressed. Finally, the implication of these results and directions for future process research are discussed.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".