Physiotherapists’ Perspectives on Professional Practice Leadership Models: Key Features to Enhance Physiotherapy Practice
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore professional practice leadership models (PPLMs) within the Toronto Academic Health Science Network (TAHSN) by outlining the PPLMs currently in use, identifying elements of the PPLMs from physiotherapists’ perspectives, and determining key features of PPLMs that enhance physiotherapy (PT) practice. Methods: In this qualitative, cross-sectional study, we used focus groups to explore physiotherapists’ knowledge about their facility’s PPLM, physiotherapists’ role within the PPLM, the impact of professional practice leaders on PT practice, the impact of the PPLM on physiotherapists’ job satisfaction, and the elements of an ideal PPLM. We coded transcripts using qualitative software and followed an inductive data analysis approach to develop themes. Results:We conducted eight focus groups with physiotherapists from six TAHSN facilities (four organizations). Five key features of PPLMs emerged from participants’ perspectives: support network, organizational structure, professional development opportunities, influence of the leader in professional practice, and balance of workloads and accountabilities. Each key feature encompassed a group of interrelated elements – that is, components of the PPLMs that influenced PT practice. Conclusions: Our study is the first to explore elements and key features of the PPLMs used in TAHSN facilities as they relate to PT. We provide five recommendations to enhance PPLMs with respect to the PT profession.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".