Determinants of Skilled Birth Attendant Utilization in Afghanistan: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: We sought to identify characteristics associated with use of skilled birth attendants where health services exist in Afghanistan. METHODS: We conducted a cross-sectional study in all 33 provinces in 2004, yielding data from 617 health facilities and 9917 women who lived near the facilities and had given birth in the past 2 years. RESULTS: Only 13% of respondents had used skilled birth attendants. Women from the wealthiest quintile (vs the poorest quintile) had higher odds of use (odds ratio [OR] = 6.3; 95% confidence interval [CI] = 4.4, 8.9). Literacy was strongly associated with use (OR = 2.5; 95% CI = 2.0, 3.2), as was living less than 60 minutes from the facility (OR = 1.5; 95% CI = 1.1, 2.0) and residing near a facility with a female midwife or doctor (OR = 1.4; 95% CI = 1.1, 1.8). Women living near facilities that charged user fees (OR = 0.8; 95% CI = 0.6, 1.0) and that had male community health workers (OR = 0.6; 95% CI = 0.5, 0.9) had lower odds of use. CONCLUSIONS: In Afghanistan, the rate of use of safe delivery care must be improved. The financial barriers of poor and uneducated women should be reduced and culturally acceptable alternatives must be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".