Learning how to rate video-recorded therapy sessions: A practical guide for trainees and advanced clinicians.
Bibliographic record
Abstract
Watching and rating psychotherapy sessions is an important yet often overlooked component of psychotherapy training. This article provides a simple and straightforward guide for using one Website (www.ATOStrainer.com) that provides an automated training protocol for rating of psychotherapy sessions. By the end of the article, readers will be able to have the knowledge to go to the Website and begin using this training method as soon as they have a recorded session to view. This article presents, (a) an overview of the Achievement of Therapeutic Objectives Scale (ATOS; McCullough et al., 2003a), a research tool used to rate psychotherapy sessions; (b) a description of APA training tapes, available for purchase from APA Books, that have been rated and scored by ATOS trained clinicians and posted on the Website; (c) step-by-step procedures on how ratings can be done; (d) an introduction to www.ATOStrainer.com where ratings can be entered and compared with expert ratings; and (e) first-hand personal experiences of the authors using this training method and the benefits it affords both trainees and experienced therapists. This psychotherapy training Website has the potential to be a key resource tool for graduate students, researchers, and clinicians. Our long-range goal is to promote the growth of our understanding of psychotherapy and to improve the quality of psychotherapy provided for patients.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.086 |
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".