Are community midwives addressing the inequities in access to skilled birth attendance in Punjab, Pakistan? Gender, class and social exclusion
Bibliographic record
Abstract
BACKGROUND: Pakistan is one of the six countries estimated to contribute to over half of all maternal deaths worldwide. To address its high maternal mortality rate, in particular the inequities in access to maternal health care services, the government of Pakistan created a new cadre of community-based midwives (CMW). A key expectation is that the CMWs will improve access to skilled antenatal and intra-partum care for the poor and disadvantaged women. A critical gap in our knowledge is whether this cadre of workers, operating in the private health care context, will meet the expectation to provide care to the poorest and most marginalized women. There is an inherent paradox between the notions of fee-for-service and increasing access to health care for the poorest who, by definition, are unable to pay. METHODS/DESIGN: Data will be collected in three interlinked modules. Module 1 will consist of a population-based survey in the catchment areas of the CMW's in districts Jhelum and Layyah in Punjab. Proportions of socially excluded women who are served by CMWs and their satisfaction levels with their maternity care provider will be assessed. Module 2 will explore, using an institutional ethnographic approach, the challenges (organizational, social, financial) that CMWs face in providing care to the poor and socially marginalized women. Module 3 will identify the social, financial, geographical and other barriers to uncover the hidden forces and power relations that shape the choices and opportunities of poor and marginalized women in accessing CMW services. An extensive knowledge dissemination plan will facilitate uptake of research findings to inform positive developments in maternal health policy, service design and care delivery in Pakistan. DISCUSSION: The findings of this study will enhance understanding of the power dynamics of gender and class that may underlie poor women's marginalization from health care systems, including community midwifery care. One key outcome will be an increased sensitization of the special needs of socially excluded women, an otherwise invisible group. Another expectation is that the poor, socially excluded women will be targeted for provision of maternity care. The research will support the achievement of the 5th Millennium Development Goal in Pakistan.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".