Bidirectional Associations Between Civic Engagement, Depressive Symptoms, and Suicidality in Youth: A Population‐Based Study
Bibliographic record
Abstract
INTRODUCTION: Civic engagement is common in youth, yet its longitudinal association with mental health remains understudied. This study aims to document bidirectional associations between civic engagement, depressive symptoms, and suicidality at 20 and 23. METHODS: We included 1451 participants born in 1997/98 from the Québec Longitudinal Study of Child Development in Canada. At 20 and 23, youth self-reported their engagement in five types of civic activities, namely political engagement, volunteering, activism, charitable actions, community involvement, depressive symptoms, and suicidality. Cross-lagged path models adjusted for sex and parental socioeconomic status were used to test associations between civic engagement and mental health outcomes. RESULTS: Concurrently, individuals who engaged in political engagement and activism were more likely to experience increases in depressive symptoms and suicidality at 20, while those who engaged in volunteering tended to report fewer depressive symptoms. At 23, political engagement remained positively associated with depressive symptoms. Longitudinal analyses revealed that individuals who engaged in activism at 20 were more likely to experience increases in depressive symptoms over time (β = 0.17) and those who participated in charitable actions (β = -0.14) and volunteering (β = -0.11) at 20 tended to report fewer depressive symptoms at 23, though these associations were not independent. Mental health at 20 was not associated to later civic engagement. CONCLUSIONS: Types of civic engagement were differentially associated with later depressive symptoms, but not suicidality. Further research is needed to clarify the mechanisms underlying these associations, including the potential role of engagement frequencies, motivations, and contextual factors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".