How Do Trainees Rate the Impact of a Short Cognitive Behavioural Training Programme on their Knowledge and Skills?
Bibliographic record
Abstract
BACKGROUND: A strong evidence base for cognitive behavioural therapy has led to CBT models becoming available within mainstream mental health services. As the concept of stepped care develops, new less intensive mental health interventions such as guided self-help are emerging, delivered by staff not trained to the level of accredited Cognitive Behavioural Therapists. AIM: The aim of this study was to determine how mental health staff evaluated the usefulness of a short training programme in CBT concepts, models and techniques for routine clinical practice. METHOD: A cohort of mental health staff (n = 102) completed pre- and posttraining self-report questionnaires measuring trainee perceptions of the impact of a short training programme on knowledge and skills. Mentors and managers were also asked to comment on perceived impact of the training. RESULTS: Trainees and mentors reported perceived gains in knowledge and skills posttraining and at 1-year follow-up. Managers and trainees reported perceived improvements in skills and practice. CONCLUSION: A short Cognitive Behavioural skills programme can enable mental health staff to integrate basic CB knowledge and skills into routine clinical practice.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".