Travel time to health facilities as a marker of geographical accessibility across heterogeneous land coverage in Peru
Bibliographic record
Abstract
ABSTRACT The geographical accessibility to health facilities is conditioned by the topography and environmental conditions overlapped with different transport facilities between rural and urban areas. To better estimate the travel time to the most proximate health facility infrastructure and determine the differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach and use as a case study the unique geographical and ecological diversity in the Peruvian territory. Geospatial data of 145,134 cities and villages and 8,067 health facilities in Peru were gathered with land coverage types, roads infrastructure, navigable river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to the most proximate health facility across the country. This study estimated important variations in travel time between urban and rural settings across the 16 major land coverage types in Peru, that in turn, overlaps with socio-economic profiles of the villages. The median travel time to primary, secondary, and tertiary healthcare facilities was 1.9, 2.3, and 2.2 folds higher in rural than urban settings, respectively. Also, higher travel time values were observed in areas with a high proportion of the population with unsatisfied basic needs. In so doing, this study provides a new methodology to estimate travel time to health facilities as a tool to enhance the understanding and characterization of the profiles of accessibility to health facilities in low- and middle-income countries (LMIC), calling for a service delivery redesign to maximize high quality of care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".