Published in:
22-07-2022 | Public Health | Original Article
Discovering Clusters of Support Utilization in the Canadian Community Health Survey–Mental Health
Authors:
Maria Cutumisu, Jordan Southcott, Chang Lu
Published in:
International Journal of Mental Health and Addiction
|
Issue 1/2024
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Abstract
Mental illness is one of the most pressing medical challenges facing society. Thus, identifying gaps in mental-health support-seeking is crucial for public health. This exploratory study aims to reveal gaps and patterns in mental-healthcare support-utilization by employing unsupervised machine learning in the Canadian Community Health Survey–Mental Health that measures support-seeking for mental-health issues from 24,788 Canadians. Of the clustering methods compared (K-means, hierarchical agglomerative, and Fuzzy C-means), Fuzzy C-means clustering yielded the best model fit for the data and revealed four clusters: No Support, Social Support, Professional Support, and Mixed Support evaluated based on existing theory. Findings reveal differential effects by all variables, except for the variable concerning whether a respondent was white or a visible minority.