Equitable Access of Naloxone Programs in Durham Region, Ontario, Canada
Bibliographic record
Abstract
Abstract Background In 2019 there were over 1500 opioid-related deaths in the province of Ontario, Canada. While the opioid crisis is affecting many socioeconomic groups and communities across Canada not all are being affected equally despite the presence of naloxone distribution programs in Ontario. This qualitative exploratory study seeks to understand facilitators and barriers that influence equitable access of naloxone programs in Durham Region, Ontario, Canada. Methods An environmental scan will be conducted to examine the availability and distribution of naloxone across community pharmacies and organizations in Durham Region. A qualitative descriptive phenomenology will be the methodological approach where key informant interviews will explore experiences of users and providers of naloxone programs. Key informants will include service providers and clients of both Ontario Naloxone Program and Ontario Naloxone Program for Pharmacies in Durham Region. The harm reduction framework will be used to guide data analysis where thematic analysis will be conducted to generate overarching themes about the phenomenon. Results The environmental scan will result in the creation of a map outlining availability and distribution of naloxone programs to examine possible gaps that exist in Durham Region. It is expected that key informant interview findings will help understand where inequity exists in accessing Ontario's naloxone programs in Durham Region by highlighting its barriers and facilitators. Conclusions Findings generated will be used for larger scale studies in the future examining equitable access of naloxone distribution programs in Canada. This study will have implications to provide recommendations to policymakers for developing new policies to facilitate timely access of naloxone to mitigate risk of opioid-related harms. Key messages This research will help to better understand the inequities that exist in Ontario's naloxone distribution programs. This research will help to inform recommendations to improve policies surrounding Ontario's naloxone distribution programs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".