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Record W4414484953 · doi:10.1111/1471-0528.70013

Interventions to Address Disparities in Perinatal Outcomes by Ethnicity: A Systematic Review

2025· review· en· W4414484953 on OpenAlexaboutno aff
Sara Sorrenti, Smriti Prasad, Nouran Elbarbary, Fathima Fidha, Laura A. Magee, Peter von Dadelszen, Sergio A. Silverio, John Allotey, Shakila Thangaratinam, Asma Khalil

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2025
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionEthnic groupMEDLINESystematic reviewIntervention (counseling)

Abstract

fetched live from OpenAlex

BACKGROUND: Ethnic minority women face disproportionately higher risks of adverse perinatal outcomes, exacerbated by socio-economic and systemic barriers. OBJECTIVES: This systematic review evaluates the effectiveness of interventions designed to improve perinatal outcomes in these populations. SEARCH STRATEGY: We conducted a systematic review according to a pre-designed protocol (PROSPERO CRD42024516616). MEDLINE, EMBASE and Cochrane Databases were searched up to February 2024 using relevant Medical Subject Headings (MeSH) terms and keywords. SELECTION CRITERIA: We included studies involving interventions targeting pregnant women from ethnic minority groups. Outcome measures included maternal and perinatal outcomes, as well as qualitative assessments, when available. DATA COLLECTION: Two reviewers independently performed data extraction and quality assessment, resolving discrepancies by consensus. MAIN RESULTS: Studies included (n = 36) were from the United Kingdom (n = 9), United States of America (n = 9), Australia (n = 12), Canada (n = 1), Denmark (n = 2), Sweden (n = 3), involving women (n = 72 527) of varied ethnicity: Asian (n = 16 274, 22.4%), Black (n = 11 458, 15.8%), Hispanic (n = 612, 0.8%), First Nations/Aboriginal (n = 19 406, 29.1%), Mixed (n = 873, 1.2%), 'Other' (as defined in the included studies) (n = 3354, 4.6%), and women belonging to an unspecified ethnic minority group (n = 15 232, 21%), and a group of Russian, Arabic, Tigrinya, Polish and Somali women in a foreign country (82 women; 0.1%). Interventions broadly included four categories: clinical management interventions, educational programmes, treatments, and models of care. Clinical management interventions like increased foetal surveillance after 39 weeks and implemented screening for preeclampsia showed positive results, with a 64% reduction in stillbirth rates among South Asian (aOR 0.36, 95% CI 0.13-0.90, p = 0.047) with the former intervention, and a decrease in perinatal deaths with the latter intervention. Educational initiatives demonstrated diverse results, with those directed to the families showing significant improvement in satisfaction and active participation in prenatal care; however, no significant improvements were noted after the implementation of initiatives devoted to healthcare providers. Specific treatments, such as low-dose aspirin, have yielded various outcomes, with some studies reporting a reduction in preterm birth rates. Models of care, including midwifery continuity of care, nutrition implementation initiatives, home visits and language support services, showed promising results in improving maternal satisfaction and obstetric outcomes. CONCLUSIONS: This systematic review summarises the interventions to improve outcomes for these families among ethnic minority women and emphasises the lack of focused attention on improving outcomes in these groups, highlighted by the limited studies and the diverse interventions and outcomes reported. While educational and social support programmes within the model of care show promise, large-scale and high-quality studies are needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.074
GPT teacher head0.454
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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