Pharmacists' experiences with dispensing opioids: provincial survey.
Bibliographic record
Abstract
OBJECTIVE: To explore pharmacists' beliefs, practices, and experiences regarding opioid dispensing. DESIGN: Mailed survey. SETTING: The province of Ontario. PARTICIPANTS: A total of 1011 pharmacists selected from the Ontario College of Pharmacists' registration list. MAIN OUTCOME MEASURES: Pharmacists' experiences with opioid-related adverse events (intoxication and aberrant drug-related behaviour) and their interactions with physicians. RESULTS: A total of 652 pharmacists returned the survey, for a response rate of 64%. Most (86%) reported that they were concerned about several or many of their patients who were taking opioids; 36% reported that at least 1 patient was intoxicated from opioids while visiting their pharmacies within the past year. Reasons for opioid intoxication included the patient taking more than prescribed (84%), the patient using alcohol or sedating drugs along with the opioid (69.9%), or the prescribed dose being too high (34%). Participants' most common concerns in the 3 months before the survey were patients coming in early for prescription refills, suspected double-doctoring, and requests for replacement doses for lost medication (reported frequently by 39%, 12%, and 16% of respondents, respectively). Pharmacists were concerned about physician practices, such as prescribing benzodiazepines along with opioids. Pharmacists reported difficulty in reaching physicians directly by telephone (43%), and indicated that physicians frequently did not return their calls promptly (28%). The strategies rated as most helpful for improving opioid dispensing were a provincial prescription database and opioid prescribing guidelines. CONCLUSION: Pharmacists commonly observe opioid intoxication and aberrant drug-related behaviour in their patients but have difficulty communicating their concerns to physicians. System-wide strategies are urgently needed to improve the safety of opioid prescribing and to enhance communication between physicians and pharmacists.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".