Community Healthcare Network in Underserved Areas: Design, Mathematical Models, and Analysis
Bibliographic record
Abstract
In community health programs implemented in underserved areas, community healthcare workers (CHWs) prevent, diagnose, and treat the most common diseases. To ensure continuous in‐service training of CHWs, some countries have mentored highly skilled CHWs to become supervisors. Designing a network in such a context implies determining the number of CHWs and supervisors, as well as the routing of the supervisors. This can be defined as a location‐routing covering problem (LRCP), a variant of the location‐routing and the covering tour problems. To solve the LRCP, we propose set‐partitioning formulations and a procedure to generate only non‐dominated variables without losing optimality, which also allows to break the symmetry between variables. Finding the most appropriate mathematical model is important to solve real‐life instances, improve the quality of the solution, and reduce the total computation time. Therefore, we develop tools to assist with the design and analysis of a community healthcare network in order to increase health coverage for underserved areas. Results are presented for an application in Liberia, including sensitivity analyses on various parameters and managerial insights.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".