Maternal deaths in Pakistan: intersection of gender, caste, and social exclusion
Bibliographic record
Abstract
BACKGROUND: A key aim of countries with high maternal mortality rates is to increase availability of competent maternal health care during pregnancy and childbirth. Yet, despite significant investment, countries with the highest burdens have not reduced their rates to the expected levels. We argue, taking Pakistan as a case study, that improving physical availability of services is necessary but not sufficient for reducing maternal mortality because gender inequities interact with caste and poverty to socially exclude certain groups of women from health services that are otherwise physically available. METHODS: Using a critical ethnographic approach, two case studies of women who died during childbirth were pieced together from information gathered during the first six months of fieldwork in a village in Northern Punjab, Pakistan. FINDINGS: Shida did not receive the necessary medical care because her heavily indebted family could not afford it. Zainab, a victim of domestic violence, did not receive any medical care because her martial family could not afford it, nor did they think she deserved it. Both women belonged to lower caste households, which are materially poor households and socially constructed as inferior. CONCLUSIONS: The stories of Shida and Zainab illustrate how a rigidly structured caste hierarchy, the gendered devaluing of females, and the reinforced lack of control that many impoverished women experience conspire to keep women from lifesaving health services that are physically available and should be at their disposal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".