Physicians’ and patients’ perceived risks of chronic pain medication and co-medications in Quebec, Canada: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The risks associated with medications and co-medications for chronic pain (CP) can influence a physician's choice of drugs and dosages, as well as a patient's adherence to the medication. High-quality care requires patients to participate in medication decisions. This study aimed to compare perceived risks of medications and co-medications between physicians and persons living with CP. METHODS: This cross-sectional survey conducted in Quebec, Canada, included 83 physicians (snowball sampling) and 141 persons living with CP (convenience sampling). Perceived risks of adverse drug reaction of pain medications and co-medications were assessed using 0-10 numerical scales (0 = no risk, 10 = very high risk). An arbitrary cutoff point of 2-points was used to ease the interpretation of our data. Physicians scored the 36 medication subclasses of the Medication Quantification Scale 4.0 (MQS 4.0) through an online survey, while CP patients scored the medication subclasses they had taken in the last three months through telephone interviews. RESULTS: Persons living with CP consistently perceived lower risks of adverse drug reaction compared to physicians. For eight subclasses, the difference in the mean perceived risk score was > 2 points and statistically significant (p < 0.05): non-specific oral NSAIDs, acetaminophen in combination with an opioid, short-acting opioids, long-acting opioids, tricyclic antidepressants, antipsychotics, benzodiazepines, and medical cannabis. CONCLUSIONS: Divergent risk perceptions between physicians and patients underscore the necessity of facilitating a more extensive discussion on medications and co-medications risks to empower patients to make informed decisions and participate in shared decision-making regarding their treatments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".