Income inequality and comorbid overweight/obesity and depression among a large sample of Canadian secondary school students: The mediator effect of social cohesion
Bibliographic record
Abstract
Background: Comorbid overweight/obesity (OWO) and depression is emerging as a public health problem among adolescents. Income inequality is a structural determinant of health that independently increases the risk for both OWO and depression among youth. However, no study has examined the association between income inequality and comorbid OWO and depression or tested potential mechanisms involved. We aimed to identify the association between income inequality and comorbid OWO and depression and to test whether social cohesion mediates this relationship. Methods: We used data from the 2018-2019 Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking and Sedentary behavior (COMPASS) project. Our sample was composed of 46,171 adolescents from 136 schools distributed in 43 census divisions in 4 provinces in Canada (Ontario, Alberta, British Columbia, and Quebec). Gender-stratified multilevel path analyses models were used to examine whether income inequality (Gini coefficient) was associated with comorbid OWO and depression and whether the association was mediated by school connectedness, a proxy measure for social cohesion. Results: income inequality was significantly associated with increased risk of comorbidity via social cohesion. One standard deviation increase in the Gini coefficient was associated with a 9% and 8% increase in the odds of comorbidity in females (OR=1.09; 95% CI=1.03, 1.16) and males (OR=1.08; 95% CI=1.03, 1.13). Conclusion: Policies aimed at reducing income inequality, and interventions to improve social cohesion, may contribute to reducing the risk of OWO-depression comorbidity among adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".