Maternal stress and hair cortisol among pregnant women following hurricane Florence
Bibliographic record
Abstract
Natural disasters represent major stressors, resulting in psychological distress and physiological responses such as increased cortisol. During pregnancy, this impacts not only maternal well-being, but also fetal development. In 2018, Hurricane Florence caused extensive damage across the eastern United States. Studies indicated that compared to married pregnant women, unmarried pregnant women had higher risk of distress. Here we assess hair cortisol among a subsample of participants, and variations based on marital status. METHODS: We analyzed multiple stress measures among 37 participants who were pregnant during Hurricane Florence. We used questionnaires modeled on previous studies to assess hardship associated with the hurricane, psychological distress, sociodemographic characteristics, social support, and food security. We analyzed cortisol concentrations in proximal and distal hair sections, representing stress around the time of the disaster (distal) and 3-4 months following the disaster (proximal). We used linear regression to test relationships between hair cortisol and self-report stress measures, and variations based on marital status. RESULTS: Self-report measures of distress and hardship were similar among married and unmarried participants. Mean cortisol levels in distal and proximal sections were higher among unmarried participants. Controlling for confounding variables, hardship was not associated with hair cortisol. Distress predicted cortisol in distal sections (β = .482, p = .018), with a trend for proximal sections (β = .368, p = .055). Marital status was a significant predictor of distal (β = .388, p = .027) and proximal (β = .333, p = .047) hair cortisol, explaining 8.6%-11.7% of unique variance. CONCLUSIONS: Preexisting and intersecting risk factors likely place unmarried pregnant individuals at risk of stress during and following a disaster.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".