Conceptually mapping how investing in essential public health functions (EPHFs) and common goods for health (CGH) can improve health system performance
Bibliographic record
Abstract
Background: Calls for investing in essential public health functions (EPHFs) and common goods for health (CGH) are numerous, but it is often unclear to policymakers how such investments lead to health system improvements. Objectives: To showcase plausible pathways between actions taken to improve specific health system functions-in other words, investments in EPHFs and CGH-and their impact on health system performance, the health systems performance assessment framework for Universal Health Coverage is used. We draw on three examples-community engagement and social participation, taxes and subsidies, and public health surveillance and monitoring-to demonstrate how action in these areas can improve health systems. Conclusions: This conceptual mapping also points to the crucial role of good governance and demonstrates how investing in multiple EPHFs and CGH can trigger a chain reaction to spur broader health system improvement.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".