Trans-National Scale-Up of Services in Global Health
Bibliographic record
Abstract
BACKGROUND: Scaling up innovative healthcare programs offers a means to improve access, quality, and health equity across multiple health areas. Despite large numbers of promising projects, little is known about successful efforts to scale up. This study examines trans-national scale, whereby a program operates in two or more countries. Trans-national scale is a distinct measure that reflects opportunities to replicate healthcare programs in multiple countries, thereby providing services to broader populations. METHODS: Based on the Center for Health Market Innovations (CHMI) database of nearly 1200 health programs, the study contrasts 116 programs that have achieved trans-national scale with 1,068 single-country programs. Data was collected on the programs' health focus, service activity, legal status, and funding sources, as well as the programs' locations (rural v. urban emphasis), and founding year; differences are reported with statistical significance. FINDINGS: This analysis examines 116 programs that have achieved trans-national scale (TNS) across multiple disease areas and activity types. Compared to 1,068 single-country programs, we find that trans-nationally scaled programs are more donor-reliant; more likely to focus on targeted health needs such as HIV/AIDS, TB, malaria, or family planning rather than provide more comprehensive general care; and more likely to engage in activities that support healthcare services rather than provide direct clinical care. CONCLUSION: This work, based on a large data set of health programs, reports on trans-national scale with comparison to single-country programs. The work is a step towards understanding when programs are able to replicate their services as they attempt to expand health services for the poor across countries and health areas. A subset of these programs should be the subject of case studies to understand factors that affect the scaling process, particularly seeking to identify mechanisms that lead to improved health outcomes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".