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Record W1562867158 · doi:10.5888/pcd12.150187

How Do You Know Which Health Care Effectiveness Research You Can Trust? A Guide to Study Design for the Perplexed

2015· article· en· W1562867158 on OpenAlexafffund
Stephen B. Soumerai, Douglas Starr, Sumit R. Majumdar

Bibliographic record

VenuePreventing Chronic Disease · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Alberta
FundersCenters for Disease Control and PreventionHarvard UniversityFondation pour la Recherche MédicaleUniversity of AlbertaAlberta InnovatesCommonwealth Fund
KeywordsMedicinePublic healthHealth promotionChronic diseaseHealth careAlternative medicineDiseasePeer reviewPromotion (chess)Family medicinePublic relationsNursingPathology

Abstract

fetched live from OpenAlex

Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.532
metaresearch head score (Gemma)0.552
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.468
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5320.552
Meta-epidemiology (narrow)0.0030.006
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0090.006
Science and technology studies0.0050.011
Scholarly communication0.0120.011
Open science0.0080.005
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0240.010

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 head0.152
GPT teacher head0.519
Teacher spread0.367 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations109
Published2015
Admission routes2
Has abstractyes

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