Towards a better tomorrow: addressing intersectional gender power relations to eradicate inequities in maternal health
Bibliographic record
Abstract
An equity lens to maternal health has typically focused on assessing the differences in coverage and use of healthcare services and critical interventions. While this approach is important, we argue that healthcare experiences, dignity, rights, justice, and well-being are fundamental components of high quality and person-centred maternal healthcare that must also be considered. Looking at differences across one dimension alone does not reflect how fundamental drivers of maternal health inequities-including racism, ethnic or caste-based discrimination, and gendered power relations-operate. In this paper, we describe how using an intersectionality approach to maternal health can illuminate how power and privilege (and conversely oppression and exclusion) intersect and drive inequities. We present an intersectionality-informed analysis on antenatal care quality to illustrate the advantages of this approach, and what is lost in its absence. We reviewed and mapped equity-informed interventions in maternal health to existing literature to identify opportunities for improvement and areas for innovation. The gaps and opportunities identified were then synthesised to propose recommendations on how to apply an intersectionality lens to maternal health research, programmes, and policies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".