Mechanisms accounting for gendered differences in mental health status among young Canadians: A novel quantitative analysis
Bibliographic record
Abstract
Adolescent girls consistently report worse mental health than boys. This study used reports from a 2018 national health promotion survey (n = 11,373) to quantitatively explore why such gender-based differences exist among young Canadians. Using mediation analyses and contemporary social theory, we explored mechanisms that may explain differences in mental health between adolescents who identify as boys versus girls. The potential mediators tested were social supports within family and friends, engagement in addictive social media use, and overt risk-taking. Analyses were performed with the full sample and in specific high-risk groups, such as adolescents who report lower family affluence. Higher levels of addictive social media use and lower perceived levels of family support among girls mediated a significant proportion of the difference between boys and girls for each of the three mental health outcomes (depressive symptoms, frequent health complaints, and diagnosis of mental illness). Observed mediation effects were similar in high-risk subgroups; however, among those with low affluence, effects of family support were somewhat more pronounced. Study findings point to deeper, root causes of gender-based mental health inequalities that emerge during childhood. Interventions designed to reduce girls' addictive social media use or increase their perceived family support, to be more in line with their male peers, could help to reduce differences in mental health between boys and girls. Contemporary focus on social media use and social supports among girls, especially those with low affluence, warrant study as the basis for public health and clinical interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".