A Scoping Review of Risk Factors of Hypertensive Disorders of Pregnancy in Black Women Living in High-Income Countries
Bibliographic record
Abstract
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are maternity-related increases in blood pressure (eg, gestational hypertension, preeclampsia, and eclampsia). Compared with women of other races in high-income countries, Black women have a comparatively higher risk of an HDP. Intersectionality helps to provide a deeper understanding of the multifactorial identities that affect health outcomes in this high-risk population. OBJECTIVE: In this review, we sought to explore the literature on HDP risk factors in Black women living in high-income countries and to assess the interaction of these risk factors using the conceptual framework of intersectionality. METHODS: We conducted this review using the Arksey and O'Malley methodology with enhancements from Levac and colleagues. Published articles in English on HDP risk factors with a sample of not less than 10% of Black women in high-income countries were included. Six databases, theses, and dissertations were searched from January 2000 to July 2021. A thematic analysis was used to summarize the results. RESULTS: A final total of 36 studies were included from the 15 480 studies retrieved; 4 key themes of HDP risks were identified: (1) biological; (2) individual traditional; (3) race and ethnicity, geographical location, and immigration status; and (4) gender related. These intersectional HDP risk factors intersect to increase the risk of HDP among Black women living in high-income countries. CONCLUSION: Upstream approaches are recommended to lower the risks of HDP in this population.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".