‘Learning from research’: Therapist perspectives on the benefits and challenges of participating in a longitudinal, systematic case‐study
Bibliographic record
Abstract
Abstract Objectives: In psychotherapy research we have often neglected to examine the impact of research procedures on therapy process and outcome. Such information is extremely important in helping us evaluate the validity of our findings, increase relevance of research for practice, and choose appropriate methods for future projects. The aim of this paper is to share the experience of six person‐centered therapists, who participated in a longitudinal, systematic study, and present their reflections about the impact of research on therapy process, therapeutic engagement and professional development. Methods: The findings have emerged from semi‐structured interviews that took place after the first, sixth (middle) and last therapy session. In total 18 interviews were conducted. These data were part of a much larger research protocol that included a number of outcome and process measures. Results & Conclusions: The analysis of therapist narratives revealed important benefits for those participating in systematic case study research. The use of Brief Structured Recall methods and qualitative interviewing was an important factor in promoting therapist reflexivity and professional development. The importance of a strong research alliance and the active involvement of the client in the research process is 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 | 0.368 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| 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".