An Exploration of Canadian Physiotherapists' Decisions about Whether to Supervise Physiotherapy Students: Results from a National Survey
Bibliographic record
Abstract
Purpose: To explore Canadian physiotherapists' perceptions of the factors that influence their decisions whether to supervise students in clinical placements. Methods: Using accepted survey development methodology, a survey was developed and administered to 18,110 physiotherapists to identify which factors contribute to the decision to supervise students. The survey also gave respondents opportunities to provide comments; these were analyzed via directed content analysis, using the factors identified in an exploratory factor analysis as an organizing structure. Results: A representative sample of 3,148 physiotherapists responded to the survey. Qualitative analysis of respondent comments provided a rich understanding of the factors contributing to the decision on whether to supervise students, which centred on themes related to stress, workplace productivity, the evaluation instrument, student preparation, and physiotherapists' professional roles and responsibilities. Challenges specific to loss of income and the ethics of charging for student services in private practice were also identified. Conclusions: Supervising students can be stressful, and stress is perceived by respondents to be most influential in deciding whether to supervise students. Effective supervisor training may mitigate some of the stresses related to supervising students. A collaborative approach involving all stakeholders is needed to resolve the issues of student placement capacity.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".