New Life Through Disaster: A Thematic Analysis of Women's Experiences of Pregnancy and the 2016 Fort McMurray Wildfire
Bibliographic record
Abstract
Background: On May 3, 2016, residents of Fort McMurray Wood Buffalo, Alberta were evacuated due to an uncontrolled wildfire. The short-notice evacuation had destabilizing consequences for residents, including changes in routines, loss of control, and increased uncertainty. These consequences were especially detrimental to women who were pregnant or pre-conception during the evacuation. Pregnant and pre-conception women are particularly susceptible to a vast range of negative consequences during and post natural disasters, including elevated stress and higher incidence of pregnancy complications including gestational diabetes mellitus, pregnancy induced hypertension and C-section. The aim of this study was to understand the experiences, perceived stress and resilience of women who were pregnant during the wildfire. As well as to explore potential interventions to promote the health and enhance resilience of pregnant women and to assist in recovery after exposure to a natural disaster or other traumatic events. Methods: A qualitative thematic analysis of 16 narratives penned by pregnant women and recounted in Ashley Tobin's compilations 93/88,000 and 159 More/ 88,000: Stories of Evacuation, Re-Entry and the In-Between was conducted. Results: Analysis revealed five key themes: (1) experience of stress responses due to personal and external factors, (2) social connectedness and support as a facilitator of resilience, (3) performance of resilience-enhancing activities, (4) the roles of pregnancy and motherhood in the experiences of loss and resilience, and (5) the importance of home. Conclusion: Pregnant women have unique barriers that may negatively impact them during a natural disaster or other form of stressful event. They may benefit from assistance with navigating role transition during pregnancy, training in stress management strategies, and writing interventions to build resiliency and begin the process of recovery from trauma.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".