Typology of adults diagnosed with mental disorders based on socio-demographics and clinical and service use characteristics
Notice bibliographique
Résumé
BACKGROUND: Mental disorder is a leading cause of morbidity worldwide. Its cost and negative impact on productivity are substantial. Consequently, improving mental health-care system efficiency - especially service utilisation - is a priority. Few studies have explored the use of services by specific subgroups of persons with mental disorder; a better understanding of these individuals is key to improving service planning. This study develops a typology of individuals, diagnosed with mental disorder in a 12-month period, based on their individual characteristics and use of services within a Canadian urban catchment area of 258,000 persons served by a psychiatric hospital. METHODS: From among the 2,443 people who took part in the survey, 406 (17%) experienced at least one episode of mental disorder (as per the Composite International Diagnostic Interview (CIDI)) in the 12 months pre-interview. These individuals were selected for cluster analysis. RESULTS: Analysis yielded four user clusters: people who experienced mainly anxiety disorder; depressive disorder; alcohol and/or drug disorder; and multiple mental and dependence disorder. Two clusters were more closely associated with females and anxiety or depressive disorders. In the two other clusters, males were over-represented compared with the sample as a whole, namely, substance abuses with or without concomitant mental disorder. Clusters with the greatest number of mental disorders per subject used a greater number of mental health-care services. Conversely, clusters associated exclusively with dependence disorders used few services. CONCLUSION: The study found considerable heterogeneity among socio-demographic characteristics, number of disorders, and number of health-care services used by individuals with mental or dependence disorders. Cluster analysis revealed important differences in service use with regard to gender and age. It reinforces the relevance of developing targeted programs for subgroups of individuals with mental and/or dependence disorders. Strategies aimed at changing low service users' attitude (youths and males) or instituting specialised programs for that particular clientele should be promoted. Finally, as concomitant disorders are frequent among individuals with mental disorder, psychological services and/or addiction programs must be prioritised as components of integrated services when planning treatment.
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 | 0,000 | 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 ».