Exploring Factors Associated with Prescribers’ Comfort Levels in Analgesic Prescribing in Quebec
Bibliographic record
Abstract
Purpose: Identifying the factors associated with comfort level when prescribing medications is important for tailoring education and training. This study aimed to explore factors associated with the comfort level of healthcare professionals regarding dispensing and adjusting prescriptions for the treatment of chronic pain (CP). Methods: A cross-sectional survey was conducted among licensed physicians, pharmacists, and nurse practitioners across the province of Quebec, Canada. Comfort level regarding dispensing and/or adjusting prescriptions for CP treatment was measured on a 0-10 rating scale (0 = very uncomfortable, 10 = very comfortable). Results: In total, 207 prescribers participated in this study (83 physicians, 58 pharmacists, and 66 nurse practitioners). 56.5% reported a comfort level in dispensing and/or adjusting prescriptions for the treatment of CP <6/10. The median comfort level score was 6 (interquartile range - IQR: 2). Differences in median scores were found between physicians (6), pharmacists (7) and nurses (5; p < 0.001). Multivariable logistic regression revealed that the factors associated with an increased likelihood of reporting a high comfort level (≥6/10) were: being a pharmacist, having a relative living with CP, a greater percentage of past year continuing educational activities about CP management, and higher perception of short-acting opioids risks. Factors associated with lower comfort levels were as follows: being a nurse practitioner, having fewer years of experience, living in a remote region, living with CP, and a higher perception of long-acting opioids risks. The practice setting and sex at birth were also associated with comfort level. Conclusion: The comfort level regarding prescribing for CP varies according to socioeconomic/professional factors, which can lead to disparities in the quality of care and outcomes for patients. Our results reinforce the importance of investing in initial training and continuing education of prescribers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".