Estimating capacity requirements for substance use treatment systems: a population-based approach
Notice bibliographique
Résumé
Treatment system planning and resource allocation is hampered by the lack of systems-level data and planning frameworks. We developed and pilot-tested a needs-based planning model for substance use services and supports that aligns with the estimated needs of the population of local health regions, takes a broad systems approach beyond the specialized sector, and yields estimates of required treatment capacities for service categories along the continuum of care. Using national population survey data, we estimated, for 94 regional planning areas in Canada, the number of people in need of substance use treatment within a given year, based on five ‘tiers’ of problem severity. We then estimated the probable help-seeking population for each level of severity, based on a synthesis of the literature. Working with a national expert consensus panel, we estimated the optimal trajectory of clients across several defined categories of treatment services organized by level of care. Integrating steps 1–3 yielded the number of people to plan for in each service setting. We piloted the model in nine Canadian jurisdictions, conducting gap analyses that compared the projected and actual service utilization, and supplemented by stakeholder feedback and local indicators of need, such as wait lists and referral data. The model development process and gap analyses at the nine pilot sites yielded important results for local planners, but with national implications. Results indicated that the capacity of moderate-intensity services is adequate in many regions, but that larger gaps exist in low-threshold services (e.g., home-based/mobile withdrawal management) and high-intensity services (e.g., medical inpatient services for high-complexity cases. These results and their implications were validated by stakeholders in the pilot sites. The needs-based planning model appears to have value in identifying local gaps in services, but regional context must be taken into account when applying the model to local jurisdictions. The piloting process highlighted a national need for systematic screening and brief intervention processes in the nonspecialized sector to improve early identification and referral of clients. We anticipate that the model will serve as a valuable tool for system planners to use in discussions and decisions about funding and resource allocation. Next steps include model adjustments using more precise regional data, developing a separate component for opiate substitution, a youth version, and incorporating the model into a larger needs assessment process. Comparable work is underway in other countries (e.g., Australia, Brazil, UK), providing opportunities for international knowledge exchange.
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 | 0,063 | 0,111 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,005 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».