Social determinants of pre-eclampsia: a scoping review and evidence map
Bibliographic record
Abstract
Background: Existing reviews of pre-eclampsia determinants have focused on clinical and genetic risk factors. Objective: To evaluate social determinants for pre-eclampsia prevention. Search strategy: Systematic searches were conducted on relevant electronic databases to 31 st July 2023. Selection criteria: Reviews and large cohort studies (≥1,000 participants), published within the last 10 years, reporting quantitative associations between social determinant exposures and pre-eclampsia outcomes. Data collection and analysis: Titles and abstracts and then relevant full-texts were reviewed by two reviewers, independently. Strength of association was evaluated as ‘definite’ (odds ratios [OR] or relative risk [RR] ≥3.00 or <0.33), ‘probable’ (OR or RR 1.50-2.99 or 0.33-0.67), ‘possible’ (OR or RR 1.10-1.49 or 0.68-0.89), or ‘unlikely’ (OR or RR 0.90 - 1.09). Quality of the evidence was high, moderate, low, or very-low, using GRADE. Main results: Twenty-six publications found 22 associations of pre-eclampsia with socioeconomic status, social support/exclusion, healthcare access, and occupational and physical environmental factors. One association (polygamy) was definite (low-quality evidence). Probable associations included: work stress and lack of antenatal care (high-quality evidence); prolonged occupational exposure to whole body vibrations or bending, elevated temperatures beyond seasonal norms, and UV-B radiation exposure (protective factor), all based on moderate-quality evidence; and Asian/Oceanian origins (protective, low-quality evidence). There were 11 possible associations, which did not include education. Conclusion: Our findings support recommendations to address climate change, strengthen occupational protection, and promote early ANC attendance. Social determinants may be indicative of upstream factors (e.g., obesity) that increase likelihood of clinical risk factors for pre-eclampsia incidence and severity.
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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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".