A sea of need: provider accounts of strategies used to manage admission demands to safer opioid supply programs in Ontario
Notice bibliographique
Résumé
BACKGROUND: Since 2016, over 50,928 people have died of an opioid-related overdose in Canada. The unregulated supply of drugs is increasingly toxic and volatile, and fentanyl from unregulated, street-based markets is driving this epidemic. Concerns that existing overdose prevention approaches were insufficient to address the rising number of overdoses led to the implementation of safer supply programs (SSPs) in Canada. SSPs provide prescribed medications to people who use drugs and are designed for individuals at high risk of overdose for whom existing care options have been ineffective or inappropriate. Evidence of SSP impact is growing but implementation processes, including admissions, are not well understood nor well-described in practice guidelines. Our purpose was to describe how the admission processes of four Ontario SSPs evolved and how these changes influenced program reach and perceived effectiveness. METHODS: During 2021, we conducted short demographic and semi-structured interviews with healthcare providers (n = 21) from four SSPs in Ontario about implementation processes, challenges, and impacts. Thematic analysis of data concerning admission processes was conducted in MAXQDA and descriptive statistics in SPSSv28. RESULTS: Although the desire was for SSPs to have a broad reach, programs quickly realized they needed to develop strategies to manage the high demand for their programs. To manage this demand, strategies were implemented like waitlists, which were later replaced by points-based admission criteria. These admission criteria evolved over time, leading to a client population with high medical and social needs. The combination of high-acuity clients, limited capacity, and funding constraints, exacerbated by COVID-19, caused significant distress and burnout among service providers, prompting further changes to the SSPs. DISCUSSION: The implementation of SSPs in Ontario highlights the challenges of addressing intersecting public health emergencies in a resource-constrained healthcare system. SSPs, were adaptive and evolved in real time; while these adaptations addressed significant equity gaps, they also underscored the limitations of operating within an under-funded primary care model. The narrowing of admission criteria, necessitated by overwhelming demand and limited resources, ultimately constrained their reach and potential population-level impact.
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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 | 0,001 | 0,001 |
| 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 ».