What clinicians want: Findings from a psychotherapy practice research network survey.
Bibliographic record
Abstract
Practice research networks may be one way of advancing knowledge translation and exchange (KTE) in psychotherapy. In this study, we document this process by first asking clinicians what they want from psychotherapy research. Eighty-two psychotherapists in 10 focus groups identified and discussed psychotherapy research topics relevant to their practices. An analysis of these discussions led to the development of 41 survey items. In an online survey, 1,019 participants, mostly practicing clinicians, rated the importance to their clinical work of these 41 psychotherapy research topics. Ratings were reduced using a principal components analysis in which 9 psychotherapy research themes emerged, accounting for 60.66% of the variance. Two postsurvey focus groups of clinicians (N = 22) aided in interpreting the findings. The ranking of research themes from most to least important were-Therapeutic Relationship/Mechanisms of Change, Therapist Factors, Training and Professional Development, Client Factors, Barriers and Stigma, Technology and Adjunctive Interventions, Progress Monitoring, Matching Clients to Therapist or Therapy, and Treatment Manuals. Few differences were noted in rankings based on participant age or primary therapeutic orientation. Postsurvey focus group participants were not surprised by the top-rated items, as they were considered most proximal and relevant to therapists and their work with clients during therapy sessions. Lower ranked items may be perceived as externally imposed agendas on the therapist and therapy. We discuss practice research networks as a means of creating new collaborations consistent with KTE goals. Findings of this study can help to direct practitioner-researcher collaborations.
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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 | 0.040 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".