A qualitative study of a publicly funded pharmacy-dispensed naloxone program
Bibliographic record
Abstract
AIMS: To characterize the experiences of individuals accessing pharmacy-based naloxone and relate these experiences to the risk environments and discourses in which they are embedded. METHODS: We conducted a qualitative study using in-depth interviews of 37 adults aged 18 years and over who had accessed pharmacy-dispensed naloxone. Participants were recruited from across Ontario, Canada, and comprised individuals taking opioids for chronic pain, those taking opioids for reasons other than chronic pain, and individuals acquiring naloxone to act as bystanders in an opioid overdose setting. We drew upon risk environment theory to interpret participants' accounts. RESULTS: Following analysis and interpretation, we generated five theoretically-informed themes characterizing the experiences of individuals accessing pharmacy-dispensed naloxone: 'intersection of naloxone narrative with pharmacy environment', 'individual risk environment and pharmacy-dispensed naloxone uptake', 'safe spaces: creating an enabling environment for pharmacy-dispensed naloxone', 'individuation: becoming a first responder' and 'beyond naloxone: the macro risk environment'. Specifically, participants described how judgement and stereotyping associated with the broader naloxone narrative can be amplified in the space of the pharmacy, leading to fears of reprisals and strategies to mitigate social risk. In addition, the social construction of naloxone as a drug for 'problematic' opioid use and a lack of pharmacist awareness regarding the risk environments in which opioid use occurs was perceived to limit opportunities for optimizing naloxone distribution and training. Finally, participants described approaches that could create enabling environments in the space of the pharmacy while remaining cognizant of the structural changes required in the macro risk environments of people who take opioids. CONCLUSIONS: Despite increasing the availability of naloxone, participants characterized several social and environmental factors that could limit the accessibility of the drug from pharmacies. Strategies to address these factors could create enabling environments within pharmacies that optimize the reach and impact of pharmacy-dispensed naloxone.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".