“They’re not doing enough.”: women’s experiences with opioids and naloxone in Toronto
Bibliographic record
Abstract
BACKGROUND: Amid increasing opioid overdose deaths in Canada since 2010 and a changing naloxone access landscape, there is a need for up-to-date research on Canadian women's experiences with opioids. Studies on Canadian take-home naloxone programs are promising, but research beyond these programs is limited. Our study is the first to focus on women's experiences and perspectives on the opioid crisis in Ontario, Canada's most populous province, since the opioid crisis began in 2010. OBJECTIVE: Our objective was to address research knowledge gaps involving Canadian women with criminal justice involvement who use opioids, and identify flaws in current policies, responses, and practices. While the opioid overdose crisis persists, this lack of research inhibits our ability to determine whether overdose prevention efforts, especially involving naloxone, are meeting their needs. METHODS: We conducted semi-structured, qualitative interviews from January to April 2018 with 10 women with experience of opioid use. They were recruited through the study's community partner in Toronto. Participants provided demographic information, experiences with opioids and naloxone, and their perceptions of the Canadian government's responses to the opioid crisis. Interviews were transcribed verbatim and inductive thematic analysis was conducted to determine major themes within the data. RESULTS: Thematic analysis identified seven major concerns despite significant differences in participant life and opioid use experiences. Participants who had used illicit opioids since naloxone became available over-the-counter in 2016 were much more knowledgeable about naloxone than participants who had only used opioids prior to 2016. The portability, dosage form, and effects of naloxone are important considerations for women who use opioids. Social alienation, violence, and isolation affect the wellbeing of women who use opioids. The Canadian government's response to the opioid crisis was perceived as inadequate. Participants demonstrated differing needs and views on ideal harm reduction approaches, despite facing similar structural issues surrounding stigma, addiction management, and housing. CONCLUSIONS: Participants experienced with naloxone use found it to be useful in preventing fatal overdose, however many of their needs with regards to physical, mental, and social health, housing, harm reduction, and access to opioid treatment remained unmet.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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".