Unassisted homebirths in Afghanistan: a qualitative exploration of underlying reasons
Bibliographic record
Abstract
Unassisted homebirths pose significant maternal health risks in Afghanistan, where nearly one-third of births occur at home without skilled birth attendants. Data from 2020 showed the maternal mortality ratio is 620 per 100,000 live births, due to pregnancy and childbirth complications. This thesis explored what factors contribute to unassisted homebirths in Afghanistan from the perspectives of Afghan women and community members. Guided by the social-ecological model, this qualitative case study examined four levels of influence: intrapersonal, interpersonal, community, and institutional. Data was collected through in-depth interviews with Afghan women who had experienced unassisted homebirths and focus group discussions with male and female community members, providing insights into the familial, cultural, and systemic factors influencing choices for birthplace and birth care. The data indicated that most interview participants had limited knowledge of pregnancy and delivery care, partly due to their lack of access to a reliable source of health information, reinforcing a preference for unassisted homebirth. Patriarchal gender norms like women’s extensive involvement in domestic or agricultural roles, restricted mobility, and limited financial autonomy, along with family dynamics—particularly the influence of elder women, especially mothers-in-law, and male relatives—further constrained women’s ability to seek skilled birth attendance. Cultural beliefs and traditions, including a preference for family-centred childbirth at home, and concerns about modesty also contributed to this preference for unassisted homebirth. Inadequate access to skilled birth attendants—due to distance, financial barriers, lack of transportation, disrespectful care, and perception of poor-quality care—contributed to the perpetuation of unassisted homebirths.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".