Physiotherapists Prefer Clinical Supervision to Focus on Professional Skill Development: A Qualitative Study
Bibliographic record
Abstract
Purpose: We explored physiotherapists’ perceptions of clinical supervision. Method: Individual semi-structured interviews were conducted with a purposive sample of 21 physiotherapists from a public hospital. Qualitative analysis was undertaken using an interpretive description approach. The Manchester Clinical Supervision Scale (MCSS–26) was administered to evaluate the participants’ perceptions of the effectiveness of the clinical supervision they had received and to establish trustworthiness in the qualitative data by means of triangulation. Results: The major theme was that the content of clinical supervision should focus on professional skill development, both clinical and non-clinical. Four subthemes emerged as having an influence on the effectiveness of supervision: the model of clinical supervision, clinical supervision processes, supervisor factors, and supervisee factors. All sub-themes had the potential to act as either a barrier to or a facilitator of the perception that clinical supervision was effective. Conclusions: Physiotherapists reported that clinical supervision was most effective when it focused on their professional skill development. They preferred a direct model of supervision, whereby their supervisor directly observed and guided their professional skill development. They also described the importance of informal supervision in which guidance is provided as issues arise by supervisors who value the process of supervision. Physiotherapists emphasized that supervision should be driven by their learning needs rather than health organization processes.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".