Experiencing trauma during or before pregnancy: qualitative secondary analysis after two natural disasters (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite the existing knowledge about stress, trauma and pregnancy and maternal stress during natural disasters which could be linked to adverse birth and child health outcomes, little is known about what types of trauma pregnant or preconception women experience during natural disasters. In May 2016, the worst natural disaster in modern Canadian history required the evacuation of nearly 90,000 residents of the Fort McMurray Wood Buffalo (FMWB) area of northern Alberta. Among the thousands of evacuees were an estimated 1850 women who were pregnant or soon to conceive. In August 2017, Hurricane Harvey devastated Texas and Louisiana, with 30,000 people forced to flee their homes due to the intense flooding. An estimated 40,000 pregnant women were living in Houston at the time of the flooding. OBJECTIVE To explore immediate and past traumatic experiences of pregnant or preconception women who experienced one of two natural disasters (a wildfire and a hurricane) as captured in their expressive writing. Research questions were: (1) What trauma did pregnant or preconception women experience during the fire and the hurricane? (2) What past traumatic experiences, apart from the disasters, did the women discuss in their expressive writing? METHODS A qualitative secondary analysis of expressive writing using thematic content analysis was conducted on the expressive writing of 50 pregnant or preconception women who experienced the 2016 Fort McMurray Wood Buffalo Wildfire (n=25) and the 2017 Houston Hurricane Harvey (n=25) Narrative data in the form of expressive writing entries from participants of two primary studies were thematically analyzed. One of the expressive writing questions was used in this analysis: “What is the most traumatic, upsetting experience of your entire life, especially that you have never discussed in great detail with others?” NVivo 12 supported thematic content analysis. RESULTS For some women, the natural disaster elicited immense fear and anxiety that surpassed past traumatic life events. Others, however, disclosed significant past traumas that continue to impact them, including betrayal by a loved one, abuse, maternal health complications, and illness. CONCLUSIONS We recommend a strengths-based and trauma-informed care approach in both maternal health and post-disaster relief care.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".