Innovation in regulation of rapidly changing health markets
Bibliographic record
Abstract
The rapid evolution and spread of health markets across low and middle-income countries (LMICs) has contributed to a significant increase in the availability of health-related goods and services around the world. The support institutions needed to regulate these markets have lagged behind, with regulatory systems that are weak and under-resourced. This paper explores the key issues associated with regulation of health markets in LMICs, and the different goals of regulation, namely quality and safety of care, value for money, social agreement over fair access and financing, and accountability. Licensing, price controls, and other traditional approaches to the regulation of markets for health products and services have played an important role, but they have been of questionable effectiveness in ensuring safety and efficacy at the point of the user in LMICs. The paper proposes a health market systems conceptual framework, using the value chain for the production, distribution and retail of health goods and services, to examine regulation of health markets in the LMIC context. We conclude by exploring the changing context going forwards, laying out implications for future heath market regulation. We argue that the case for new approaches to the regulation of markets for health products and services in LMICs is compelling. Although traditional "command and control" approaches will have a place in the toolkit of regulators, a broader bundle of approaches is needed that is adapted to the national and market-level context of particular LMICs. The implication is that it is not possible to apply standard or single interventions across countries, as approaches proven to work well in one context will not necessarily work well elsewhere.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".