A qualitative examination of trainee perspectives on cognitive behavioural supervision
Bibliographic record
Abstract
Abstract Clinical supervision is the main method by which mental health professionals acquire the competence to deliver safe and effective therapy. The cognitive behavioural supervision (CBS) approach to supervision parallels CBT in structure and form, which may facilitate learning. Although supervision is integral to trainee development, little is known about what CBS interventions trainees consider helpful. Using a qualitative content analysis methodology, we aimed to identify the specific CBS interventions that trainees find most helpful. Eight trainees completing a CBT rotation in an out-patient hospital setting received weekly individual supervision by staff psychiatrists and psychologists. Following each supervision meeting, trainees completed open-ended responses describing what they found most and least helpful. Responses from 127 meetings were coded using a CBS framework. Overall, trainees found many aspects of supervision helpful. The interventions most frequently noted as valuable were teaching, planning, formulating, training/experimenting, and evaluation of their work. When trainees mentioned unhelpful events, insufficient collaboration and a desire for more or less supervision structure were most frequently noted. These results suggest that the perceived helpfulness of supervision may be tied to the use of CBS interventions that provide trainees with concrete skills that facilitate learning. Further suggestions and implications for supervisors are discussed. Key learning aims (1) To identify the aspects of cognitive behavioural supervision that trainees perceive as most and least helpful for their learning. (2) To integrate trainees’ perspectives with the existing research on supervision satisfaction. (3) To consider limitations, challenges and future directions of cognitive behavioural supervision research.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".