Addressing disparities in maternal health care in Pakistan: gender, class and exclusion
Bibliographic record
Abstract
BACKGROUND: After more than two decades of the Safe Motherhood Initiative and Millennium Development Goals aimed at reducing maternal mortality, women continue to die in childbirth at unacceptably high rates in Pakistan. While an extensive literature describes various programmatic strategies, it neglects the rigorous analysis of the reasons these strategies have been unsuccessful, especially for women living at the economic and social margins of society. A critical gap in current knowledge is a detailed understanding of the root causes of disparities in maternal health care, and in particular, how gender and class influence policy formulation and the design and delivery of maternal health care services. Taking Pakistan as a case study, this research builds upon two distinct yet interlinked conceptual approaches to understanding the phenomenon of inequity in access to maternal health care: social exclusion and health systems as social institutions. METHODS/DESIGN: This four year project consists of two interrelated modules that focus on two distinct groups of participants: (1) poor, disadvantaged women and men and (2) policy makers, program managers and health service providers. Module one will employ critical ethnography to understand the key axes of social exclusion as related to gender, class and zaat and how they affect women's experiences of using maternal health care. Through health care setting observations, interviews and document review, Module two will assess policy design and delivery of maternal health services. DISCUSSION: This research will provide theoretical advances to enhance understanding of the power dynamics of gender and class that may underlie poor women's marginalization from health care systems in Pakistan. It will also provide empirical evidence to support formulation of maternal health care policies and health care system practices aimed at reducing disparities in maternal health care in Pakistan. Lastly, it will enhance inter-disciplinary research capacity in the emerging field of social exclusion and maternal health and help reduce social inequities and achieve the Millennium Development Goal No. 5.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".