Exploring social inequalities in the use and experience of prenatal care in Brussels
Bibliographic record
Abstract
Abstract Background The underlying mechanisms of perinatal health inequalities remain under-researched. A possible explanation is inadequate access to healthcare services and suboptimal care for certain groups. To explore this, our study analyses the mothers’ prenatal care trajectories and experiences of care in relation to socioeconomic and migration characteristics. Methods A survey of 750 migrant and non-migrant mothers interviewed in 4 maternity wards in Brussels. Based on the adaptation of the Migrant-Friendly Maternity Care Questionnaire, the survey focuses on mothers with a Belgian, North African (NA) and Subsaharan African (SSA) nationality. The data are analysed through descriptive statistics and logistic regression. Results This ongoing survey has a high response rate (83%). Preliminary subsample analyses (n = 276) show that the socioeconomic profiles of mothers vary substantially depending on nationality, with mothers from NA generally having a lower level of education, and mothers from SSA being strongly at risk of poverty. SSA women are particularly at risk of starting prenatal care late (32% compared to 4% of Belgians and NAs) and of having less than 7 recommended consultations (25% compared to 6% Belgians and 9% NAs). Low household income and maternal education were also strong predictors of late and infrequent prenatal care. Women from all three nationality groups had the same rate (10%) of planned C-sections, but women from SSA were much more likely to have an emergency c-section (16% vs 7% of Belgians and NAs). Women with a higher household income were less likely to have a c-section at all. Women from both NA and SSA were less likely to always have understood the information given by healthcare professionals. 90% of women with secondary school education understood all the information, compared to 60% of women without it. Conclusions Women’s care trajectories and experiences of care vary strongly depending on their nationality and socioeconomic situation. Key messages North African and Subsaharan African immigrants in Brussels live in different socioeconomic situations. Their care trajectories and experiences of care also show important differences. Socioeconomic characteristics, in addition to women’s migration status, are important factors affecting women’s perinatal care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".