Developing a National Consensus of the Physiotherapy Entry-Level Business and Practice Management Core Curriculum Competencies: A Delphi Study
Bibliographic record
Abstract
Purpose: ; however, there is little consistency in how these are applied across academic institutions. The purpose of this study was to develop a set of foundational entry-to-practice (ETP) competencies related to business and practice management (BPM) that can prepare physiotherapy students for work in all Canadian health care service sectors upon graduation. Method: We undertook a modified Delphi study. An online call for participants was circulated via the Canadian Physiotherapy Association's Private Practice and Leadership Divisions, Canadian provincial and territorial physiotherapy regulators, and 15 Canadian university physiotherapy programmes. Individuals in the profession with known expertise in management and/or business were also invited to participate. Results: Two rounds of the Delphi were necessary to reach consensus. Forty-one participants were included in the first round of Delphi including academics, regulators, registered physiotherapists, and senior students. Twenty-one (51%) participated in round 2 of the study. Sixty-six ETP BPM foundational curriculum competencies, within nine domains, reached consensus (via the Delphi process). Conclusions: Consideration of the ETP competencies in the areas of business and practice management derived with this national Delphi process may enhance and harmonize the physiotherapy curricula across Canada.
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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 | 0.104 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".