Medication recommendation by physiotherapists: A survey of Québec physiotherapists' opinions regarding a new interprofessional model of care with pharmacists
Bibliographic record
Abstract
BACKGROUND: To improve the efficiency of the health care system, new interprofessional models of care are emerging. In 2015, two provincial professional colleges, regulating the practice of physiotherapists and that of pharmacists in the province of Québec, Canada, developed a new interprofessional model of care. This model is designed to guide non-prescription medication recommendations by physiotherapists treating patients in primary care with neuromusculoskeletal disorders (NMSKD) with the collaboration of pharmacists. PURPOSE: To assess Québec physiotherapists' interests to use this model and explore their opinions concerning their ability to recommend non-prescription medications to patients in primary care with NMSKD. METHODS: ) were performed to compare proportions (%) across demographic and clinical characteristics. RESULTS: Two hundred twenty-five physiotherapists completed the full survey. Of these, 70% of respondents knew of the model of care, but only 15% had previously used it. Perceived workload increase was one major reason reported for this lack of use (51%). Most of the respondents had a positive perception of this model and interactions with pharmacists and were confident regarding their ability to safely recommend medication (63%). However, 63% believed that further training was necessary to enable physiotherapists to provide efficient and safe non-prescription medication recommendations to patients with NMSKD. CONCLUSIONS: Overall, physiotherapists have a positive perception of this model, but there remain opportunities for increased integration into practice. Most respondents believe that additional training is required regarding non-prescription medication recommendations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".