Assessing health program performance in low- and middle-income countries: building a feasible, credible, and comprehensive framework
Bibliographic record
Abstract
BACKGROUND: Many health service delivery models are adapting health services to meet rising demand and evolving health burdens in low- and middle-income countries. While innovative private sector models provide potential benefits to health care delivery, the evidence base on the characteristics and impact of such approaches is limited. We have developed a performance measurement framework that provides credible (relevant aspects of performance), feasible (available data), and comparable (across different organizations) metrics that can be obtained for private health services organizations that operate in resource-constrained settings. METHODS: We synthesized existing frameworks to define credible measures. We then examined a purposive sample of 80 health organizations from the Center for Health Market Innovations (CHMI) database (healthmarketinnovations.org) to identify what the organizations reported about their programs (to determine feasibility of measurement) and what elements could be compared across the sample. RESULTS: The resulting measurement framework includes fourteen subgroups within three categories of health status, health access, and operations/delivery. CONCLUSIONS: The emphasis on credible, feasible, and comparable measures in the framework can assist funders, program managers, and researchers to support, manage, and evaluate the most promising strategies to improve access to effective health services. Although some of the criteria that the literature views as important - particularly population coverage, pro-poor targeting, and health outcomes - are less frequently reported, the overall comparison provides useful 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 | 0.168 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".