Income inequality and depression among Canadian secondary students: Are psychosocial well-being and social cohesion mediating factors?
Bibliographic record
Abstract
Nearly one-third of secondary school students report experiencing depressive symptoms in the past year. Existing research suggests that increasing rates of depression are due in part to increasing income inequality. The aim of this study is to identify mechanisms by which income inequality contributes to depression among Canadian secondary school students. We used data from a large sample of Canadian secondary school students that participated in the 2017/18 wave of the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) study. The sample included 61,642 students across 43 Census divisions (CDs) in Quebec, Ontario, Alberta, and British Columbia. We used multilevel path analysis to determine if the relationship between CD-level income inequality and depression was mediated by student's psychosocial well-being and/or social cohesion. Attending schools in CDs with higher income inequality was related to higher depression scores among Canadian secondary students [unstandardized ß (ß) = 5.36; 95% CI = 0.74, 9.99] and lower psychosocial well-being (ß = −14.83, 95% CI = −25.05, −4.60). Income inequality was not significantly associated with social cohesion, although social cohesion was associated with depression scores among students (ß = −0.31; 95% CI = −0.34, −0.28). Findings from this study indicate that income inequality is associated with adolescent depression and that this relationship is mediated by psychosocial well-being. This study is the first of its kind in Canada to assess the mechanisms by which income inequality contributes to adolescent depression. These findings are applicable to school-level programs addressing mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".