Pregnant women's psychological responses to climate change: a rapid review
Bibliographic record
Abstract
Introduction Climate change can elicit a range of emotional responses and may increase the prevalence of mental health disorders, with pregnant women being more susceptible. The aim of this review was to summarize the existing literature on prevalent emotional responses to climate change and associated mental health conditions among pregnant women. Methods A rapid review was conducted using a search strategy adapted from the University of Alberta's pre-made standardized search filter for pregnant women. Four databases were searched on the 13th of October 2023: Ovid MEDLINE, Ovid Embase, EBSCO GreenFILE, and Ovid PsycINFO. Results No studies assessed emotional and mental health issues among pregnant women based on perception of climate change. However, the seven included studies made assessments based on climate change-related events (e.g., flood, hurricane, tsunami, typhoon, and wildfire). Climate change-related events resulted in emotional distress (e.g., anger, fear, and peritraumatic distress) and mental health conditions (e.g., depression, anxiety, and post-traumatic stress disorder). These relationships may be mediated through social support, resilience, and coping styles. Also, the intensity of the emotional distress was influenced by stressful life events (e.g., neighbourhood violence, prolonged separation from family) that were directly or indirectly associated with the experienced climate change-related events. Conclusion Emerging research on climate impacts on emotional and mental well-being of pregnant women has focused predominantly on climate-related events. A significant gap remains in understanding the effects of perception of climate change. Our review findings draw attention to social support, resilience, and coping strategies as potential adaptative strategies to climate crises.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".