Maternal Mental Health after a Wildfire: Effects of Social Support in the Fort McMurray Wood Buffalo Study
Bibliographic record
Abstract
OBJECTIVE: Following disasters, perinatal women are vulnerable to developing post-traumatic stress disorder (PTSD)-like symptoms. Little is known about protective factors. We hypothesized that peritraumatic stress would predict PTSD-like symptoms in pregnant and postpartum women and would be moderated by social support and resilience. METHOD: = 200) who experienced the 2016 Fort McMurray Wood Buffalo wildfire during or shortly before pregnancy completed the Peritraumatic Distress Inventory (PDI), Peritraumatic Dissociative Experiences Questionnaire, and the Impact of Event Scale-Revised for current PTSD-like symptoms. They also completed scales of social support (Social Support Questionnaire-Short Form) and resilience (Connor-Davidson Resilience Scale). RESULTS: = 0.56) correlated with more severe PTSD-like symptoms. Greater social support satisfaction was associated with less severe post-traumatic stress symptoms but only when peritraumatic distress was below average; at more severe levels of PDI, this psychosocial variable was not protective. CONCLUSIONS: Maternal PTSD-like symptoms after a wildfire depend on peritraumatic distress and dissociation. Higher social support satisfaction buffers the association with peritraumatic distress, although not when peritraumatic reactions are severe. Early psychosocial interventions may protect perinatal women from PTSD-like symptoms after a wildfire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".