Perspectives on Diversion of Medications From Safer Opioid Supply Programs
Notice bibliographique
Résumé
Importance: Safer supply programs were implemented in Canada to provide pharmaceutical-grade alternatives to the toxic unregulated drug supply. While research shows clinical benefits and reduced overdose mortality among safer supply patients, medication diversion remains a concern. Objective: To examine provider (prescribing clinicians and allied health professionals) and patient perspectives on diversion of opioids prescribed in safer supply programs. Design, Setting, and Participants: In 2021, qualitative interviews and sociodemographic questionnaires were conducted with patients and providers across 4 safer supply programs in Ontario, Canada. Interviews with 21 providers (physicians, nurse practitioners, and allied health professionals) and 52 patients examined experiences implementing safer supply or receiving care. Initial data analysis was conducted from December 2021 to March 2022, and the subanalysis focused on diversion was conducted from December 2023 to March 2024. Exposures: Participation in safer supply program as a patient or provider. Main Outcomes and Measures: Data about diversion were coded, extracted, and thematically analyzed. Results: Of 52 patient participants, 29 (55.8%) were men and 23 (44.2%) were women; 1 was Black (1.9%), 9 (17.3%) were Indigenous, 1 was Latino (1.9%), and 41 (78.8%) were White; and the mean (SD) age was 46.5 (9.6) years. Of 21 provider participants, 6 (28.6%) were men, 13 (61.9%) were women, and 2 (9.5%) were nonbinary; and the mean (SD) age was 37.6 (7.6) years. Participants characterized diversion as a spectrum ranging from no diversion, to occasional medication sharing and loss, to selling all prescribed doses of safer supply (considered rare and easy to detect). Most patients reported they consumed all or most of their prescribed medications and rarely shared or sold their doses. However, providers and patient participants shared that people might share, trade, and/or sell some of their medications with other opioid-using people for multiple reasons. Most prominent reasons for diversion were (1) compassionate sharing with intimate partners and friends to manage withdrawal and overdose risk; (2) selling or trading medications to address their own unmet substance use needs (eg, high opioid tolerance); and (3) medication loss due to poverty, homelessness, and associated vulnerabilities to theft and coercion. Programs used nonpunitive urine drug screening practices and patient self-report to monitor medication use. When diversion was identified, providers described using nonjudgmental conversations to understand patients' needs and develop mitigation strategies that addressed underlying reasons for diversion, including changing doses and medications prescribed to better match patients' needs, enrolling eligible intimate partners, and developing safety plans to mitigate vulnerabilities to theft and coercion. Conclusions and Relevance: Diversion encompasses a wide spectrum of practices (selling, sharing, and loss of medications), and occurs for complex reasons that surveillance and punitive measures are unlikely to mitigate. Diversion may be best addressed by expanding medication options to better match patients' diverse substance use needs and high tolerance, alongside wraparound social supports.
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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) | 0,001 | 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 ».