Examining Early Life Trauma as a Contributing Factor to Later Stressor Burden: A Focus on Discriminatory and Social Determinant Stress
Bibliographic record
Abstract
Experiences of early-life trauma can have detrimental long-term effects on wellbeing and may contribute to increases in later stressful life experiences, such as discrimination stress and/or unmet social determinants of health.The overarching goal of the current study was to understand how early-life trauma sets off a cascade of stressful experiences throughout life to affect mental health, and identify which ethnic and gender groups would be most affected.Participants (N = 471) comprised individuals who identified as one of four ethnicity groups: Black, White, Middle Eastern and East Asian.All participants completed questionnaires on-line assessing stressful life experiences and mental health.Black individuals displayed higher levels of everyday discrimination compared to East Asian and Middle Eastern individuals.As well, both Black and White individuals reported higher levels of lifetime discrimination compared to East Asian individuals.For Black individuals, everyday discrimination mediated the relationship between early-life trauma and anxiety but not to depressive symptoms, a relation that was found among White and Middle Eastern individuals.Importantly, discrimination did not mediate any relationships among East Asian individuals.Social determinants of health mediated the relationships between both early-life trauma and anxiety, and early-life trauma and depression among East Asian, Black, and White individuals.Moreover, gender was a significant moderator in the mediated relationship between early-life trauma, everyday discrimination, and mood scores.These moderated mediation relationships were significant for both women and men but were stronger for men.The current study reveals important pathways that could help disentangle the contributions of early-life trauma to later stressors and mental health challenges for specific gender and ethnic groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".