Correcting Psychotherapists' Blindsidedness: Formal Feedback as a Means of Overcoming the Natural Limitations of Therapists
Bibliographic record
Abstract
PURPOSE: Monitoring of client progress in psychological therapy using formal outcome measures at each session has been shown to increase the effectiveness of treatment. It seems likely that this 'feedback' effect is achieved by enabling therapists to identify clients at risk of treatment failure so that therapists can pay greater attention to client difficulties, which may be hindering therapeutic work. To date, little attention has been given to understanding relevant mechanisms of formal feedback in psychological therapy. In order to understand and maximize the benefits of feedback, it is essential to explore potential mechanisms contributing to this effect. Research in social psychology may help to explain how feedback works. METHODS: Findings on cognitive biases in the field of social psychology are explored and linked to preliminary findings in the field of psychotherapy research. RESULTS: Research on cognitive biases and expertise is congruent with indications that clinical prediction in psychotherapy is unreliable and that it may be difficult for clinicians to detect errors in their judgement as a result of a lack of clear corrective feedback. This problem is linked to the fact that clinical outcomes occur in a complex 'noisy' environment where prediction is inherently difficult. CONCLUSION: Formal feedback may derive its benefits from its ability to help correct naturally occurring biases in therapists' assessment of their work. If these biases are seen as normal, but often avoidable if feedback is used, this may pave the way to greater acceptance of formal feedback by clinicians and enhanced outcomes for clients. KEY PRACTITIONER MESSAGE: The use of formal feedback tools can help therapists overcome inevitable limitations in their ability to predict poor response to treatment, enhancing the likelihood of detecting and resolving client difficulties in therapy.
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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 | 0.045 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".