Examining cross-lag associations between perceived stress and hair cortisol from pregnancy to 15 months postpartum
Bibliographic record
Abstract
BACKGROUND: The perinatal period is often characterized by heightened psychological and biological stress. While psychological distress and neuroendocrine (hypothalamic pituitary adrenal, HPA) activity are often assumed to be interrelated, perceived stress and hair cortisol concentrations, a marker of longer-term HPA activity, are inconsistently associated during the perinatal period. This longitudinal study investigates the concurrent, prospective, and cross-lagged associations between maternal perceived stress and HCC from pregnancy through 15 months postpartum. METHODS: Individuals (n = 304) participated at different points during pregnancy and at 6 weeks, 6 months, and 15 months postpartum. At each time point, self-reported perceived stress and hair samples were collected. Correlational analyses and cross-lagged panel analyses were used to evaluate the stability of perceived stress and hair cortisol over time, as well as the concurrent and cross-lagged associations. RESULTS: Both perceived stress and HCC showed strong stability across time points. The only significant cross-lagged association was between higher HCC at 6 weeks postpartum predicting higher perceived stress at 6 months. No other cross-lagged or concurrent associations between HCC and perceived stress were significant. CONCLUSIONS: These findings highlight the stability of maternal perceived stress and HCC from pregnancy to 15 months postpartum. We did not find strong support for concurrent or cross-lagged associations between HCC and perceived stress during the perinatal period, suggesting potential distinct biological and psychosocial contributors to psychological and neuroendocrine markers of stress.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".