Early-life adversity, later-life mental health, and resilience resources: a longitudinal population-based birth cohort analysis
Bibliographic record
Abstract
BACKGROUND: Robust and persistent links between early-life adversities and later-life mental distress have previously been observed. Individual and social resources are associated with greater mental health and resilience. This study aimed to test these resources as moderators and mediators of the association between childhood psychosocial adversity and later-life mental distress. METHODS: Participant data came from the Medical Research Council National Survey of Health and Development, a nationally-representative birth cohort study. The General Health Questionnaire-28 (GHQ-28) captured mental distress at ages 53, 60-64, and 68-69. An eight-item cumulative psychosocial adversity score was created (0, 1, 2, ≥3 adversities). Individual (i.e., education, occupational status, physical activity) and social (i.e., social support, neighborhood cohesion) resources were examined as mediators and moderators of CPA and GHQ-28 in longitudinal multilevel models. FINDINGS: < 0·001, 95% CI 0·011, 0·022). Lower mental distress was associated with higher levels of physical activity, occupational status, education, social support, and neighborhood cohesion. There was no evidence that resources moderated the relationship between GHQ-28 and adversity. All resources, save for physical activity and occupational status, partly mediated this relationship. CONCLUSIONS: Individual and social resources were associated with lower mental distress. They did not modify, but partly mediated the association between childhood adversity and adult mental distress. Social support was the most important mediator, suggesting that interventions to promote greater social support may offset psychosocial adversities experienced in childhood to foster better mental health in older adults.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".