Investigating Community Pharmacy Take Home Naloxone Dispensing during COVID-19: The Impact of One Public Health Crisis on Another
Bibliographic record
Abstract
A recent report found that the number of opioid-related deaths in Ontario in the first 15 weeks of the COVID-19 pandemic was 38.2% higher than in the 15 weeks before the pandemic. Our study sought to determine if pharmacy professionals self-reported an increase or decrease in naloxone provision due to the pandemic and to identify adjustments made by pharmacy professionals to dispense naloxone during the pandemic. A total of 231 Ontario community pharmacy professionals completed an online survey. Pharmacy professionals' barriers, facilitators, and comfort level with dispensing naloxone before and during the pandemic were identified. The sample consisted of mostly pharmacists (99.1%). Over half (51.1%) reported no change in naloxone dispensing, while 22.9% of respondents reported an increase and 24.7% a decrease. The most common adjustments made during the pandemic were training patients how to administer naloxone over video or phone, delivering naloxone kits, and pharmacy technicians offering naloxone at prescription intake. Over half (55%) of participants said the top barrier for dispensing was that patients did not request naloxone. Naloxone distribution through pharmacies could be further optimized to address the increased incidence of overdose deaths during the pandemic. Future research should investigate the reasons for changes in naloxone dispensing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".