Social Determinants of Maternal Health in Afghanistan: A Review
Bibliographic record
Abstract
INTRODUCTION: Afghanistan has a high maternal mortality rate of 400 per 100,000 live births. Although direct causes of maternal morbidity and mortality in Afghanistan include hemorrhage, obstructed labor, infection, high blood pressure, and unsafe abortion, the high burden of diseases responsible for maternal mortality arises in large part due to social determinants of health. The focus of this literature review is to examine the impact of various social determinants of health on maternal health in Afghanistan, filling an important gap in the existing literature. METHODS: This narrative review was conducted using Arksey and O'Malley's framework of (1) defining the question, (2) searching the literature, (3) assessing the studies, (4) synthesizing selected evidence in context, and (5) summarizing potential programmatic implication of the context. We searched Medline, CABI global health database, and Google Scholar for relevant publications. RESULTS: A total of 38 articles/reports were included in this review. We found that social determinants such as maternal education, sociocultural practices, and social infrastructure have a significant impact on maternal health. Health care may be the immediate determinant, but it is influenced by other determinants that must be addressed in order to alleviate the burden on health care, as well as to achieve long-term reduction in maternal mortality. CONCLUSION: Because of the importance of social factors for maternal health outcomes, committed involvement of multiple government sectors (i.e. education, labor and social affairs, information and culture, transport and rural development among others, alongside health care) is the long-term solution to the maternal health problems in Afghanistan. National and international organizations' long-term commitment to social investment such as education, local economy, cultural change, and social infrastructure is recommended for Afghanstan and globally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".