Perceptions on the Essential Competencies for Intraprofessional Practice
Bibliographic record
Abstract
PURPOSE: To gather the perspectives of physiotherapists and physiotherapist assistants on essential competencies for intraprofessional (physiotherapist-physiotherapist assistant) collaboration. METHOD: A survey was developed to gather physiotherapist and physiotherapist assistant perceptions of the essential elements of effective and efficient intraprofessional collaborative practice. Participants were asked to rate the importance of 36 elements in 6 different categories (communication, collaboration, consultation, assignment of tasks, conflict management, and roles/responsibilities) involved in intraprofessional practice. RESULTS: A total of 1049 physiotherapists and 121 physiotherapist assistants responded to the survey. Analysis identified 10 competency elements perceived by participants as essential to effective and efficient intraprofessional collaboration. Comparisons using demographic variables consistently yielded the same top 10 elements. CONCLUSIONS: Our results indicated that physiotherapists and physiotherapist assistants working in private and public practice share very similar views on what is essential for effective intraprofessional practice. The consensus is that communication is key; open lines of communication help to determine responsibilities. Physiotherapy pre-licensure and continuing education programmes should include opportunities to work on communication, listening, and the skills needed to interact and collaborate effectively.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".