“They’re not doing enough.”: women’s experiences with opioids and naloxone in Toronto
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».