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Record W2069896504 · doi:10.1371/journal.pmed.1001185

Guidance for Evidence-Informed Policies about Health Systems: Rationale for and Challenges of Guidance Development

2012· article· en· W2069896504 on OpenAlexaff
Xavier Bosch‐Capblanch, John N. Lavis, Simon Lewin, Rifat Atun, John‐Arne Røttingen, Daniel Dröschel, Lise Beck, Edgardo Ábalos, Fadi El‐Jardali, Lucy Gilson, Sandy Oliver, Kaspar Wyss, Peter Tugwell, Regina Kulier, Tikki Pang, Andy Haines

Bibliographic record

VenuePLoS Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of OttawaInstitute of Population and Public HealthMcMaster University
FundersAlliance for Health Policy and Systems ResearchWorld Health OrganizationRockefeller Foundation
KeywordsLow and middle income countriesHealth policyHealthcare systemDeveloping countryGlobal healthEvidence-based medicineMEDLINEMedicinePolitical scienceEconomic growthEngineering ethicsPublic healthHealth careNursingLawEconomicsEngineering

Abstract

fetched live from OpenAlex

In the first paper in a three-part series on health systems guidance, Xavier Bosch-Capblanch and colleagues examine how guidance is currently formulated in low- and middle-income countries, and the challenges to developing such guidance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.151
GPT teacher head0.381
Teacher spread0.230 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations140
Published2012
Admission routes1
Has abstractyes

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