Correlates of residential detoxification completers and non-completers in Alberta
Notice bibliographique
Résumé
OBJECTIVES: We aimed to identify factors correlated with completion, non-completion and cycling between completion and non-completion among clients admitted to Alberta Residential Withdrawal Management Services. METHODS: The study included data on clients from a provincial database (N = 20,020) admitted to residential withdrawal management units across Alberta between April 1, 2015, and March 31, 2022. We collapsed admission-level data into client-level data, where each individual's information is captured as a unique record. Multinomial logistic regression was performed to investigate correlates of completion, non-completion, and multiple cycles of completion and non-completion. Clients in these three categories were compared in their socio-demographics, primary substance of concern, and other program variables of admission count, length of stay, transition to treatment, facilities utilized, days of discharge, and years of admission. RESULTS: The study sample included 39,952 admissions, with an average of two per client. Overall, 55.8% (n = 11,170) of discharged clients completed the program, 25.5% (n = 5,106) were non-completed, and 18.7% (n = 3,744) cycled between completion and non-completion. Regression analysis indicated that clients who used a single substance, primarily alcohol, cocaine and marijuana, completed post-secondary education, were employed or had unstable employment, and were married had lower odds of non-completion. Other variables related to higher odds of completion were urban residence, multiple admissions to a facility, and longer lengths of stay. Conversely, clients who primarily used amphetamines, barbiturates, crystal meth, opiates, and tranquillizers had higher odds of non-completion. Female gender, being discharged on Saturday and Sunday, using detoxification as a standalone service without transitioning to residential treatment and admission to two or more facilities also correlated with higher odds of non-completion. CONCLUSION: Results indicated that the type of substances, gender, education, marital status, employment, place of residence, and transition to residential services were associated with detoxication outcomes. These findings can inform the customization and allocation of services, targeted support, service intensity and areas requiring additional attention and investment to improve treatment outcomes.
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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 ».