Opioid prescribing and dispensing: Experiences and perspectives from a survey of community pharmacists practising in the province of Quebec
Bibliographic record
Abstract
BACKGROUND: Canada leads in opioid prescription and consumption rates, and this has resulted in high levels of opioid-related morbidity and mortality. Pharmacists' input could contribute significantly to understanding the disadvantages of opioid prescribing and dispensing and improving the service. This study aimed to examine the experiences of community pharmacists in relation to opioid prescribing and dispensing, with a focus on optimizing collaboration and communication. METHODS: An online survey was performed among pharmacists from the province of Quebec, Canada, in 2016. Pharmacists were eligible if registered and working in community pharmacies. RESULTS: In all, 542 questionnaires were analyzed (participation rate of 8.1%). Pharmacotherapy-related problems were reported in at least 50% of opioid prescriptions: additional drug(s) required (reported by 30% of pharmacists), interaction(s) between opioid(s) and other drug(s) (16%), physician did not meet the general issuing standards for opioid prescriptions (26%) and patient had mild to moderate pain that was easily managed by a nonopioid analgesic (20%). Half of the patients were reported as requesting anticipated refills, possibly indicating abuse or poor pain control. Most pharmacists (89.6%) reported needing to contact physicians in 1 to 3 out of 10 opioid prescriptions, but many pharmacists (71.8%, often or very often) reported difficulties communicating with physicians. CONCLUSIONS: Pharmacists' observations of pharmacotherapy-related problems and patients' unusual behaviours reveal a significant number of issues related to opioid prescribing and dispensing in an outpatient setting. Improved collaboration between physicians and pharmacists appears mandatory to address the issues reported in this study.
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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 | 0.001 |
| 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".