Knowledge and Use of, and Attitudes toward, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in Practice: A Survey of Ontario Physiotherapists
Bibliographic record
Abstract
Purpose: To investigate Ontario physiotherapists' knowledge and use of, and attitudes toward, non-steroidal anti-inflammatory drugs (NSAIDs) to identify whether there is a need for physiotherapists to receive education specific to NSAIDs. Method: An existing survey instrument was modified and tested by five Ontario physiotherapists. The final version was distributed electronically to approximately 4,400 Ontario Physiotherapy Association members as a self-administered online questionnaire. Results: A total of 294 physiotherapists responded to the survey (response rate=6.7%). Respondents demonstrated variability in their knowledge of NSAID contraindications, side effects, and drug interactions. Most respondents (62.6%) were incorrect or unsure about where and how to obtain most NSAIDs, and most demonstrated incorrect or uncertain knowledge of the relevant legislation. Despite this lack of knowledge, 50% of respondents recommend NSAIDs to their patients. Conclusions: Many Ontario physiotherapists who participated in this survey recommend NSAIDs to their patients despite having a variable understanding of the legislation and medication-related factors. A lack of thorough knowledge of risks and contraindications has implications for patient safety. Physiotherapists who incorporate medications into their practice should access comprehensive information on appropriate NSAID use and should inform themselves about legislative restrictions to ensure that associated treatment is provided in a manner that is evidence based, safe, and in keeping with regulatory boundaries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".