Validation and Pattern Discovery in the Canadian Community Health Survey - Mental Health (CCHS-MH) Support Utilization
Notice bibliographique
Résumé
Mental illness is one of the most pressing medical challenges facing society. Although identifying gaps in mental-health support utilization is important for public health, this topic has not been widely explored in the literature. The latest Canadian Community Health Survey - Mental Health Component on mental-health support utilization was conducted by the Canadian government and sampled 24,788 Canadians. It collected information on twelve mental-health support utilization items and nine sociodemographic items, namely province of residence, residence in a metropolitan/non-metropolitan area, age, sex, marital status, visible minority status, immigrant status, highest level of attained education, and household income. However, this instrument has not been validated yet. Hence, this research aims to 1) probe the structural validity and reliability of the CCHS-MH instrument using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA); 2) use clustering unsupervised machine learning algorithms to find patterns of mental-health support utilization by grouping participants based on their support utilization; and 3) compare and contrast these patterns using chi-square analyses to examine group differences in demographic characteristics. Findings show that the reliability (i.e., internal consistency) of the measure was adequate (α = .79). There is agreement among the EFA, CFA, and clustering analyses in revealing a 4-factor optimal model fit and in the nature of the factors: No Support, Social Support, and Professional Support were always relevant. The fourth factor, Mixed Support, which combines professional and social support systems, seems to yield the best fit, as reflected by the CFA. The final model yields 4 factors underlying mental-health support utilization: No Support, Social Support, Professional Support, and Mixed Support. The findings also show that Fuzzy C-Means clustering outperform the other two clustering algorithms employed (K-Means and Hierarchical Agglomerative Clustering). Post-hoc analyses found significant differential patterns of utilization in every demographic variable, except for visible minority status. Theoretical implications include support for the validation and reliability of a 4-factor model of the CCHS-MH support utilization and for the effectiveness of Fuzzy C-Means Clustering in finding patterns underlying large quantities of psychological data. Practical implications include more evidence for established patterns of support utilization observed in both the Canadian and global context as well as campaigns to encourage communities to talk openly about mental health, reverse biases in the field, and emphasize mental health in medical training.
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 ».