Maternal marital status predicts self‐reported stress among pregnant women following hurricane Florence
Bibliographic record
Abstract
OBJECTIVES: The effects of stress caused by natural disasters may be more pronounced in individuals with preexisting disadvantages. The degree of hardship and psychological distress associated with Hurricane Florence was assessed in 83 pregnant women. This research helps identify unmarried pregnant women as a group particularly at risk of distress following a natural disaster. METHODS: We assessed hardship associated with the hurricane using a questionnaire modeled on previous studies of stress due to natural disasters. We assessed distress using the Impact of Event Scale-Revised. We assessed social support and household food security using validated questionnaires. We used hierarchical linear regression to test predictors of distress marital status. Finally, we analyzed interactions between marital status and hardship, social support, and food security to examine whether these variables explained differences in distress among married and unmarried women. RESULTS: Results indicated that unmarried women may be at higher risk of distress following natural disasters. Unmarried women were younger, had lower food security and education levels. We found no differences between experiences of hurricane-related hardship based on marital status. However, unmarried women were likely to have higher levels of distress following the hurricane. Hardship was a significant predictor of distress, but food security and social support were not significant predictors. CONCLUSIONS: This study identifies unmarried pregnant women as a high risk/vulnerable group that may need additional support during emergencies. Taken together, this study further assesses how socially disadvantaged members of society may be unequally impacted by natural disasters.
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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".