Pre-Licensure Inter-Professional Perspectives: Pelvic Health Physiotherapy
Bibliographic record
Abstract
Purpose: In this study, we examined knowledge and perspectives pertaining to pelvic health physiotherapy among medical, midwifery, nursing, and physician assistant students at McMaster University. Moreover, we identified opportunities to improve knowledge translation to facilitate inter-professional education in urogynecological care. Method: A cross-sectional design was created to distribute an online survey to participants using a modified Dillman approach. The survey assessed areas of clinical interest in, knowledge of the scope of practice of, and regulations governing pelvic health physiotherapy in specific, in addition to clinical applications. Fisher’s exact and Kruskal–Wallis tests were used to assess statistical significance. Results: A total of 90% of the participants incorrectly indicated that internal digital exams could be delegated to physiotherapy assistants, and 50% believed that Kegel exercises were appropriate for all presentations of pelvic floor dysfunction. Moreover, when prompted to select conditions that could be treated by pelvic health physiotherapists, only 2% of the participants selected the correct conditions. Conclusions: Knowledge in all four programmes about the scope of practice, authorized activities, and application of pelvic health physiotherapy is inadequate. To foster the optimal integration of urogynecology into the relevant health science curriculums, enhanced inter-professional education, inclusive of pelvic health physiotherapy knowledge, appears to be needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".