Impact of policy changes on the provision of naloxone by pharmacies in Ontario, Canada: a population‐based time–series analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: In June 2016, the Ontario, Canada government implemented the Ontario Naloxone Program for Pharmacies (ONPP), authorizing pharmacists to provide injectable naloxone kits at no charge to all Ontario residents. In March 2018, the program was amended to include intranasal naloxone and remove the requirement to present a government health card to the dispensing pharmacist. We examined whether these changes increased naloxone dispensing through the ONPP. DESIGN: Population-based time-series analysis using interventional autoregressive integrated moving average models. SETTING: Ontario, Canada. PARTICIPANTS: All Ontario residents between 1 July 2016 and 31 March 2020. MEASUREMENTS: Monthly rates of pharmacy naloxone dispensing. FINDINGS: Overall, 199 484 individuals were dispensed a naloxone kit during the study period. In the main analysis, the rate of pharmacy naloxone dispensing increased by 65.1% following program changes (55.6-91.8 kits per 100 000 population between February 2018 and May 2018; P = 0.01). In subgroup analyses, naloxone dispensing increased among individuals receiving opioid agonist therapy (OAT) (3374.9-7264.2 kits per 100 000 OAT recipients; P = 0.04) among individuals receiving other prescription opioids (192.8-381.8 kits per 100 000 population prescribed opioids; P < 0.01), among individuals with past opioid exposure (134.7-205.6 kits per 100 000 population with past opioid exposure; P < 0.01) and in urban centers (56.2-91.4 kits per 100 000 population; P < 0.01). We did not observe a clear impact on pharmacy-dispensed naloxone to individuals with no or unknown opioid exposure (34.4-39.3 kits per 100 000 population with no/unknown opioid exposure; P = 0.42) and in rural regions (50.4-97.2 kits per 100 000 population; P = 0.09). CONCLUSIONS: Changes to the Ontario Naloxone Program for Pharmacies to add intranasal naloxone and remove the requirement to present a government health card appeared to increase pharmacy-based naloxone dispensing uptake in Ontario, Canada, particularly among individuals at high risk of inadvertent opioid overdose.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".