Balancing the Scales: Factors Shaping Physical Therapy Clinical Education Capacity
Bibliographic record
Abstract
Purpose: To explore the current perspectives of physiotherapists regarding the factors and opportunities for increasing clinical education (CE) capacity in the Greater Toronto Area. Method: For our study design we used a cross-sectional qualitative descriptive design with focus group methodology to gain a thorough understanding of PTs’ attitudes, enablers, and barriers to engage in CE. Subsequently, we used the six-step thematic analysis process proposed by Braun and Clarke. Results: We conducted five focus groups for a total of 15 participants. Five themes emerged from the data: workplace efficiency, workplace factors, university factors, student factors, and clinician factors. Clinicians’ attitudes were found to be motivators to engage in CE. However, the perceived or actual decrease that hosting a student has on the clinical instructor's work efficiency was stated as a barrier. Conclusions: The current health care shortage in Canada, exacerbated by the COVID-19 pandemic, justifies the need to train more physiotherapists. As more studies explore the factors influencing the decision to engage in CE, an ongoing collaborative approach between the different stakeholders in CE is necessary to increase the capacity for placement opportunities.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".