Early-life stress, depressive symptoms, and inflammation: the role of social factors
Bibliographic record
Abstract
Objective To identify modifiable, social factors that moderate the relationship between early-life stress (ELS) and health outcomes as measured by depressive symptoms and inflammation.Methods Data were from 3,416 adults (58.28% female), ages 36 - 97 (Mage= 68.41; SDage= 10.24) who participated in the 2006 wave of the Health and Retirement Study, a nationally representative sample of older adults in the United States. This study used hierarchical regression analyses to first test the main effects of ELS on depressive symptoms and inflammation (high-sensitivity C-reactive protein). Four social factors (perceived support, frequency of social contact, network size, and volunteer activity) were assessed as moderators of the ELS-depression and ELS-inflammation relationships.Results There was a small, positive association between ELS and depressive symptoms (B = 0.17, SE = 0.05, p = .002), which was moderated by social contact and perceived support. Specifically, ELS was only associated with elevated depressive symptoms for participants with limited social contact (B = 0.24, SE = 0.07, p < .001) and low perceived support (B = 0.24, SE = 0.07, p < .001). These associations remained after accounting for potential confounds (age, body-mass index, adulthood stress, and marital status).Conclusions Increased social contact and perceived support may be protective for individuals at a higher risk of developing depressive symptoms as a result of ELS. Future interventions may benefit from leveraging these social factors to improve quality of life in adults with ELS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".