Are Canadian Physiotherapy Graduates Ready for Private Practice? Faculty, Employer, and Recent Graduate Perspectives
Bibliographic record
Abstract
Purpose: The transition from physiotherapy student to independent practitioner is challenging. New graduates experience difficulties working in private practice as many aspects of the workflow are difficult to prepare for during their education. The purpose of our study was to explore the work readiness of Canadian new graduate physiotherapists for private practice from the perspectives of key groups. Method: We administered an online questionnaire to recent Canadian physiotherapy graduates, private practice employers, and academics that explored the work readiness of new graduates related to competencies and constructs relevant to private practice. Results: Our findings highlight gaps in education related to the business of private practice, managing complex caseloads, diagnosis, prognosis and establishing a plan of care, and autonomy in decision-making. A shared responsibility exists for the work readiness of physiotherapy graduates. Respondents suggest at least 1 year of practice before the majority of graduates are work-ready for private practice. Conclusion: Physiotherapy graduates struggle with the business of working in private practice, managing complex caseloads, and autonomy in decision-making. There is a shared responsibility between academics, clinical educators, employers, and new graduates to bridge the gap between the entry-to-practice education programme and meeting the expectations of the private practice work environment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".