What tools are available to assess climate and environmental health impacts on perinatal families with an equity lens? A rapid review of the Canadian context
Bibliographic record
Abstract
OBJECTIVES: This rapid review is designed to identify existing tools in the Canadian literature that assess the impacts of climate change on the health of perinatal families, particularly those who are equity-denied. Addressing the needs of equity-denied perinatal populations in the face of climate change is crucial to promoting equitable and inclusive perinatal care in Canada. METHODS: Rapid review methodology was selected to provide evidence in a timely and cost-effective manner. PubMed/MEDLINE and gray literature (Google and Google Scholar) were searched for English and French papers published from 2013 onward. The original research question, focused on climate change and health, yielded very few relevant results. Therefore, the search was broadened to include environmental health. Garrity et al.'s (J Clin Epidemiol 130:13-22, 2021) nine-stage process was used to identify 11 relevant papers, extract the relevant data, and complete the narrative synthesis. SYNTHESIS: This review revealed a significant lack of tools for comprehensively assessing climate-health impacts on perinatal families and equity-denied perinatal families. While Canadian perinatal health screenings focus on equity via indicators of several social determinants of health (e.g., income, social support), they largely omit climate considerations. Environmental health factors are more commonly included but remain minimal. CONCLUSION: Climate-health screening tools are lacking yet needed in routine perinatal healthcare. Given the seriousness of climate change, urgent engagement of health systems and healthcare workers is essential to help mitigate and adapt to climate-health challenges, particularly for perinatal families experiencing health inequities.
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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 | 0.044 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.037 | 0.036 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".