Engaging, recruiting, and retaining pregnant people from marginalized communities in environmental health cohort studies: a scoping review
Bibliographic record
Abstract
Abstract Objectives To identify barriers to and strategies for improving the representation of pregnant people from marginalized communities in pregnancy cohort studies that measure environmental chemicals. Methods Guided by the Arksey O’Malley and Levac Frameworks, we conducted a scoping review of peer-reviewed literature published between 2000 and 2022. Included studies discussed barriers and/or strategies related to engaging, recruiting, and retaining pregnant participants or participants of reproductive age from marginalized communities into environmental health research. Results Twenty-nine peer-reviewed articles were included in the review. Overall, 31% (9/29) of the studies reported on engagement, recruitment, and retention of participants from racialized communities, 10% (3/29) reported on involvement of participants identifying as Indigenous, and 10% (3/29) of studies reported on participants living in households or areas of low socioeconomic status. We identified four key barriers: participant burden, social inequities, lack of trust, and lack of cultural relevance. We reported identified strategies to mitigate these barriers. Conclusion Although there is limited coverage in the literature on strategies to effectively engage people from marginalized communities in environmental health pregnancy cohort studies, our findings suggest that applying a health equity and social justice lens to research may help address barriers that exist at the individual, interpersonal, community, institutional, and policy levels. Findings from this review may have important implications for planning future pregnancy cohort studies and ensuring that communities who are disproportionately affected by environmental chemical exposures may be better represented in research and considered in policy decisions.
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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 | 0.089 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".