A novel application of a data mining technique to study intersections in the social determinants of mental health among young Canadians
Bibliographic record
Abstract
OBJECTIVES: Adolescent mental health is an emergent clinical and public health priority in Canada. Gender-based differences in mental health are well established. The objective of this study was to evaluate a new data mining technique to identify social locations of young Canadians where differences in mental health between adolescent males and females were most pronounced. METHODS: We examined reports from 21,221 young Canadians aged 11-15 years (10,349 males, 10,872 females) who had responded to a 2018 national health and health behaviours survey. Using recursive partitioning for subgroup identification (SIDES), we identified social locations that were associated with the strongest differences between males and females for three reported mental health outcomes: positive psychosomatic health, symptoms of depression, and having a diagnosed mental illness. RESULTS: The SIDES algorithm identified both established and new intersections of social factors that were associated with gender-based differences in mental health experiences, most favouring males. DISCUSSION: This analysis represents a novel proof-of-concept to demonstrate the utility of a subgroup identification algorithm to reveal important differences in mental health experiences between adolescent males and females. The algorithm detected new social locations (i.e., where gender intersected with other characteristics) associated with poor mental health outcomes. These findings set the stage for further intersectional research, involving both quantitative and qualitative analyses, to explore how axes of discrimination may intersect to shape potential gender-based health inequalities that emerge during childhood.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 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".