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Record W2146903852 · doi:10.1186/s12961-015-0007-x

Evaluation as evolution: a Darwinian proposal for health policy and systems research

2015· article· en· W2146903852 on OpenAlexaff
Alexandra Martiniuk, Ṣẹ̀yẹ Abímbọ́lá, Merrick Zwarenstein

Bibliographic record

VenueHealth Research Policy and Systems · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitute for Clinical Evaluative SciencesWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsHealth policyHealth services researchHealth administrationCompetition (biology)Health careSet (abstract data type)Social policyDarwinismMetaphorPoliticsComputer scienceRisk analysis (engineering)Operations researchBusinessManagement scienceEconomicsPolitical scienceLawEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.272
metaresearch head score (Gemma)0.077
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2720.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0080.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.949
GPT teacher head0.813
Teacher spread0.136 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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