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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".