Longitudinal analysis of lifetime stressors and depression: Exploring intersectionality and tailoring social support for better mental health in a community population cohort
Bibliographic record
Abstract
AIMS: Health inequalities studies need to understand how individuals simultaneously defined by several socioeconomic factors differ from others when facing a series of stressors across the lifespan in the risk of major depression (MD). Theoretical efforts, as well as empirical studies, have suggested a pertinent role of social support in mental health outcomes. However, little is known about which forms of social support would alleviate the negative impact of MD vulnerability in self-rated mental health (SRMH) across different socioeconomic groups. We investigated 1) differential associations between lifetime stressors and MD across social groups and 2) explored diverse social support forms mediating the associations between MD vulnerability and SRMH. METHODS: Data analyzed were from a large longitudinal population-based cohort. Multilevel analysis of individual heterogeneity and discriminatory accuracy was used to articulate MD vulnerability in different social groups defined by ethnicity, gender, and socioeconomic status (SES). Genetic predispositions were also included in the modeling process. These social groups were then regrouped based on their vulnerability level of MD. Mediation analyses were then applied to identify which social support forms mediate the effect of MD vulnerability on SRMH. RESULTS: Higher levels of stressors were associated with higher risks of MD, and their associations varied by different social groups. The social groups (White men with medium SES or White women with high SES) had the lowest predicted incidence of MD, whereas White women with low SES reported the highest predicted incidence of MD. Two social support forms (guidance and opportunity for nurturance) significantly mediated the indirect paths between MD vulnerability and SRMH. CONCLUSIONS: By applying an intersectional lens, the present study provides a novel quantitative instrument for documenting the associations of stress and depression in various social identities. The findings of the study suggest more focused intervention programs and strategies for risk reduction should focus on identified characteristics and pay particular attention to the combined effect of lifetime stressors and discovered social identities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".