Use of a geographic information system to assess accessibility to health facilities providing emergency obstetric and newborn care in Bangladesh
Bibliographic record
Abstract
OBJECTIVE: To use a geographic information system (GIS) to determine accessibility to health facilities for emergency obstetric and newborn care (EmONC) and compare coverage with that stipulated by UN guidelines (5 EmONC facilities per 500 000 individuals, ≥1 comprehensive). METHODS: A cross-sectional study was undertaken of all public facilities providing EmONC in 24 districts of Bangladesh from March to October 2012. Accessibility to each facility was assessed by applying GIS to estimate the proportion of catchment population (comprehensive 500 000; basic 100 000) able to reach the nearest facility within 2 hours and 1 hour of travel time, respectively, by existing road networks. RESULTS: The minimum number of public facilities providing comprehensive and basic EmONC services (1 and 5 per 500 000 individuals, respectively) was reached in 16 and 3 districts, respectively. However, after applying GIS, in no district did 100% of the catchment population have access to these services. A minimum of 75% and 50% of the population had accessibility to comprehensive services in 11 and 5 districts, respectively. For basic services, accessibility was much lower. CONCLUSION: Assessing only the number of EmONC facilities does not ensure universal coverage; accessibility should be assessed when planning health systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".