Improvement in the Short-Term Effectiveness of the Clinical Supervision of Physiotherapists Who Have Taken Part in a Clinical Supervision Training Programme
Bibliographic record
Abstract
Purpose: The purpose of this study was to evaluate change in the effectiveness of clinical supervision of physiotherapists who took part in a clinical supervision training programme. Method: Our pre–post study design used both quantitative and qualitative methods. The programme consisted of three interactive sessions held with physiotherapists from a metropolitan public health network in Melbourne, Victoria, Australia. The effectiveness of clinical supervision of supervisees was measured using the Manchester Clinical Supervision Scale (MCSS–26). The effectiveness of clinical supervision from the supervisors’ perspective was measured using a clinical supervisor questionnaire. The physiotherapists’ experience of participating in the training programme was then explored in focus groups. Results: A total of 36 physiotherapists participated in the training programme. Twelve weeks later, the physiotherapists (35) reported a moderate improvement in the effectiveness of clinical supervision, with a mean improvement of 5.4 units (95% CI: 2.0, 8.9; p = 0.003) on the MCSS–26 (score range 0–104). After training, a higher proportion of physiotherapists reported receiving effective clinical supervision (97% after vs. 53% before; p = 0.001). In the focus groups, the physiotherapists reported greater flexibility in their approach to clinical supervision and a more effective supervisory relationship. However, difficulty finding time for supervision remained a barrier. Conclusions: Physiotherapists reported an improvement in the effectiveness of clinical supervision after a clinical supervision training programme.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".