Using a Data-Driven Population Segmentation Approach to Improve our Understanding of Mental Health and Associated Mental Healthcare Service Use Patterns in Ontario, Canada.
Bibliographic record
Abstract
Mental health is a multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress and well-being. There are complex relationships between social determinants of mental health, dimensions of mental health and mental healthcare service use that are not well captured using single indicators or measures. The aim of this thesis was to use a multidimensional data-driven approach to leverage existing population datasets to improve our understanding of mental health and mental health service use in the Ontario population. Through developing data-driven measures of mental health and service use, we further aim to uncover and, in part, explain social disparities in mental health. The first study of this thesis applies a data-driven clustering approach to administrative healthcare data holdings at ICES to identify distinct patterns of use of publicly funded mental health services in Ontario. We identified six patterns of mental healthcare use that were characterized by different intensity and combinations of types of services. The second study uses a data-driven clustering approach with the 2012 Canadian Community Health Survey, Mental Health, to identify unique mental health profiles in Ontario and their associated mental health service use patterns. We found four mental health profiles exhibiting notable differences in mental health and well-being. These profiles were linked to differential likelihood of service use as well as sociodemographic differences, including different age, employment, and income profiles. The final study of this thesis uses a causal decomposition approach to quantify the extent to which differences in mental health profiles between men and women are underpinned by employment. We found that men and women exhibited different combinations of flourishing, life stress and clinical disorders. Some of these differences were explained by differences in employment among men and women, however employment differences were also found to be suppressing gender differences in positive mental health and well-being. Taken together, these results provide data-driven insights into mental health as a multidimensional concept among the Ontario population, including social determinants of mental health and how mental health problems manifest in unique service use patterns. These findings can inform policies and strategies designed to improve population mental health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".