Evaluation as evolution: a Darwinian proposal for health policy and systems research
Bibliographic record
Abstract
BACKGROUND: Health systems are complex and health policies are political. While grand policies are set by politicians, the detailed implementation strategies which influence the shape and impact of these policies are delegated to technical personnel. This is an underappreciated opportunity for optimising health systems. We propose that selective 'breeding' through successive evaluations of and selection among implementation strategies is a metaphor that health system thinkers can use to improve health care. DISCUSSION: Similar to Darwinian evolution, the acceptance and accumulation of successful choices and the detection and discarding of unsuccessful ones would improve health systems in small and uncontroversial ways, over time. The effects of better implementation choices would be synergistic and cumulative, accumulating large impact (and lessons) from small changes. Just as with evolution of species, this means that even slight improvements over usual outcomes makes these numerous small choices as important a focus for system improvement as the overarching policy itself. Several alternative implementation approaches can be compared under real-world conditions in prospective head-to-head experimental and non-experimental explorations to understand whether and to what extent a strategy works and what works for whom, how, and under what circumstances in different locations. As in breeding or evolution, the best variants would spread to become the new, proven superior, implementation strategies for that policy in those settings. CONCLUSIONS: Evolution does not produce a new species whole, in a single transaction. Instead it gathers new parts and powers over time as different combinations are tested through competition with one another, to survive and spread or become extinct. Without necessarily changing or challenging grand policies, extending this idea to health systems innovation can facilitate thinking around how local, small - but cumulative - improvements in implementation potentially contribute to a pattern of successive adaptation spreading within its viable niche and ultimately providing locally-derived, long-term improvements in health systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.272 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".