A Pan-Canadian Perspective on Education and Training Priorities for Physiotherapists. Part 1: Foundations for Clinical Practice
Bibliographic record
Abstract
Purpose: Canadian physiotherapists who participated in the Physio Moves Canada (PMC) project of 2017 identified the current state of training programmes as a threat facing professional growth of the discipline in Canada. One purpose of this project was to identify key priority areas for physiotherapist training programmes as identified by academics and clinicians across Canada. Method: The PMC project included a series of interviews and focus groups conducted across clinical sites in every Canadian province and in the Yukon Territory. Data were interpreted using descriptive thematic analysis; identified sub-themes were returned to participants for reflection. Results: Overall, 116 physiotherapists and 1 physiotherapy assistant participated in 10 focus groups and 26 semi-structured interviews. Participants identified critical appraisal of continuing professional development options, knowledge translation, cultural fluency, professionalism, pharmaceutical knowledge, and clinical reasoning as priorities. For clinical practice specifically, participants identified practical knowledge, scope of practice, exercise prescription, health promotion, care of complex patients, and digital technologies as the priorities. Conclusion: Training priorities identified by participants may be useful to physiotherapy educators in preparing graduates to be adaptable and flexible primary health care providers for the future needs of a diverse population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".