The Fort McMurray Mommy Baby Study: A Protocol to Reduce Maternal Stress Due to the 2016 Fort McMurray Wood Buffalo, Alberta, Canada Wildfire
Bibliographic record
Abstract
Introduction:Data show that maternal stress triggered by exposure to a natural disaster before, during or just after pregnancy is associated with adverse pregnancy and newborn outcomes. In this paper, the first aim is to describe our efforts to test a simple, low-cost intervention to large numbers of women following a major natural disaster. The second aim is to outline the challenges faced and lessons learned during the execution of this natural disaster study. Methods:The setting was the May 2016 Fort McMurray Wood Buffalo wildfire in northern Alberta, Canada. Women who were pregnant or preconception at the time of the disaster were invited to participate via social media. This prospective cohort study included a randomized controlled trial to test the effectiveness of an expressive writing intervention on the levels of prenatal maternal stress and maternal, birth, and early childhood outcomes. At recruitment and at multiple timepoints postpartum, a battery of questionnaires was administered to evaluate objective and subjective stress exposure to the fire as well as maternal mental health, resilience and its contributing factors as well as infant developmental milestones. Qualitative content analysis of the expressive writing was conducted. Discussion:There is an increasing need to develop effective, wide-spread, rapid, and low-cost interventions to reduce prenatal maternal stress, increase resilience, and improve pregnancy outcomes following a natural disaster. Though analysis of data is ongoing, we highlight the strengths of this study which include strong community participation, rapid recruitment of eligible participants, low-cost intervention and data acquisition, and successful testing of the intervention. We acknowledge the challenges we encountered including the high rate of participant disqualifications or losses due to incomplete collection of online data; evacuation, dispersal, and inconsistent return to homes; and the high levels of stress accumulated post-disaster which led to inability to complete the study. Despite potential challenges, there remains a need for such research amid natural disasters.
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 | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".