Insights from Drug Checking Programs: Practicing Bootstrap Public Health Whilst Tailoring to Local Drug User Needs
Notice bibliographique
Résumé
The year 2021 was the most deadly year for overdose deaths in the USA and Canada. The stress and social isolation stemming from the COVID-19 pandemic coupled with a flood of fentanyl into local drug markets created conditions in which people who use drugs were more susceptible to accidental overdose. Within territorial, state, and local policy communities, there have been longstanding efforts to reduce morbidity and mortality within this population; however, the current overdose crisis clearly indicates an urgent need for additional, easily accessible, and innovative services. Street-based drug testing programs allow individuals to learn the composition of their substances prior to use, averting unintended overdoses while also creating low threshold opportunities for individuals to connect to other harm reduction services, including substance use treatment programs. We sought to capture perspectives from service providers to document best practices around fielding community-based drug testing programs, including optimizing their position within a constellation of other harm reduction services to best serve local communities. We conducted 11 in-depth interviews from June to November 2022 via Zoom with harm reduction service providers to explore barriers and facilitators around the implementation of drug checking programs, the potential for integration with other health promotion services, and best practices for sustaining these programs, taking the local community and policy landscape into account. Interviews lasted 45-60 min and were recorded and transcribed. Thematic analysis was used to reduce the data, and transcripts were discussed by a team of trained analysts. Several key themes emerged from our interviews: (1) the instability of drug markets amid an inconsistent and dangerous drug supply; (2) implementing drug checking services in dynamic environments in response to the rapidly changing needs of local communities; (3) training and ongoing capacity building needed to create sustainable programs; and (4) the potential for integrating drug checking programs into other services. There are opportunities for this service to make a difference in overdose deaths as the contours of the drug market itself have changed over time, but a number of challenges remain to implement them effectively and sustain the service over time. Drug checking itself represents a paradox within the larger policy context, putting the sustainability of these programs at risk and challenging the potential to scale these programs as the overdose epidemic worsens.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».