Apples Don't Fall Far From the Tree: Influences on Psychotherapists' Adoption and Sustained Use of New Therapies
Bibliographic record
Abstract
The purpose of this investigation was to identify influences on the current clinical practices of a broad range of mental health providers as well as influences on their adoption and sustained use of new practices.U.S. and Canadian psychotherapists (N=2,607) completed a Web-based survey in which they rated factors that influence their clinical practice, including their adoption and sustained use of new treatments.Empirical evidence had little influence on the practice of mental health providers. Significant mentors, books, training in graduate school, and informal discussions with colleagues were the most highly endorsed influences on current practice. The greatest influences on psychotherapists' willingness to learn a new treatment were its potential for integration with the therapy they were already providing and its endorsement by therapists they respected. Clinicians were more often willing to continue to use a new treatment when they were able to effectively and enjoyably conduct the therapy and when their clients liked the therapy and reported improvement.Implications for dissemination and sustained use of new psychotherapies by community psychotherapists are discussed. For example, evidence-based treatments may best be promoted through therapy courses and workshops, beginning with graduate studies; to ensure future use of new therapies, developers of training workshops should emphasize ways to integrate their approaches into clinicians' existing practices.
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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 | 0.011 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".