Proposing standardised geographical indicators of physical access to emergency obstetric and newborn care in low-income and middle-income countries
Bibliographic record
Abstract
Emergency obstetric and newborn care (EmONC) can be life-saving in managing well-known complications during childbirth. However, suboptimal availability, accessibility, quality and utilisation of EmONC services hampered meeting Millennium Development Goal target 5A. Evaluation and modelling tools of health system performance and future potential can help countries to optimise their strategies towards reaching Sustainable Development Goal (SDG) 3: ensure healthy lives and promote well-being for all at all ages. The standard set of indicators for monitoring EmONC has been found useful for assessing quality and utilisation but does not account for travel time required to physically access health services. The increased use of geographical information systems, availability of free geographical modelling tools such as AccessMod and the quality of geographical data provide opportunities to complement the existing EmONC indicators by adding geographically explicit measurements. This paper proposes three additional EmONC indicators to the standard set for monitoring EmONC; two consider physical accessibility and a third addresses referral time from basic to comprehensive EmONC services. We provide examples to illustrate how the AccessMod tool can be used to measure these indicators, analyse service utilisation and propose options for the scaling-up of EmONC services. The additional indicators and analysis methods can supplement traditional EmONC assessments by informing approaches to improve timely access to achieve Universal Health Coverage and reach SDG 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".