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.
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Acknowledgements
We would like to thank the following granting agencies for their support: Social Sciences and Humanities Research Council of Canada—Insight Development Grant (SSHRC IDG) RES0034954 and Insight Grant (SSHRC IG) RES0048110, the Natural Sciences and Engineering Research Council (NSERC DG) RES0043209, the Killam Cornerstone Operating Grant RES0043207, and the CanCode Cybera Inc. (Callysto) Grant RES0059331.
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Cutumisu, M., Southcott, J. & Lu, C. Discovering Clusters of Support Utilization in the Canadian Community Health Survey–Mental Health. Int J Ment Health Addiction 22, 394–416 (2024). https://doi.org/10.1007/s11469-022-00880-4
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DOI: https://doi.org/10.1007/s11469-022-00880-4