Recent Canadian efforts to develop population-level pregnancy intervention studies to mitigate effects of natural disasters and other tragedies
Bibliographic record
Abstract
The preconception, pregnancy and immediate postpartum and newborn periods are times for mothers and their offspring when they are especially vulnerable to major stressors - those that are sudden and unexpected and those that are chronic. Their adverse effects can transcend generations. Stressors can include natural disasters or political stressors such as conflict and/or migration. Considerable evidence has accumulated demonstrating the adverse effects of natural disasters on pregnancy outcomes and developmental trajectories. However, beyond tracking outcomes, the time has arrived for gathering more information related to identifying mechanisms, predicting risk and developing stress-reducing and resilience-building interventions to improve outcomes. Further, we need to learn how to encapsulate both the quantitative and qualitative information available and share it with communities and authorities to mitigate the adverse developmental effects of future disasters, conflicts and migrations. This article briefly reviews prenatal maternal stress and identifies three contemporary situations (wildfire in Fort McMurray, Alberta, Canada; hurricane Harvey in Houston, USA and transgenerational and migrant stress in Pforzheim, Germany) where current studies are being established by Canadian investigators to test an intervention. The experiences from these efforts are related along with attempts to involve communities in the studies and share the new knowledge to plan for future disasters or tragedies.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".