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Record W2765628342 · doi:10.1186/s13012-017-0656-y

Criteria for selecting implementation science theories and frameworks: results from an international survey

2017· article· en· W2765628342 on OpenAlexaff
Sarah A. Birken, Byron J. Powell, Christopher M. Shea, Emily R. Haines, M. Alexis Kirk, Jennifer Leeman, Catherine L. Rohweder, Laura J. Damschroder, Justin Presseau

Bibliographic record

VenueImplementation Science · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Allergy and Infectious DiseasesNational Center for Advancing Translational SciencesNational Institute of Mental HealthNational Institutes of Health
KeywordsCLARITYFalsifiabilityHealth services researchManagement scienceProcess (computing)Consistency (knowledge bases)Health informaticsImplementation researchHealth administrationComputer scienceData scienceMedicinePublic healthEpistemologyPsychological interventionNursingArtificial intelligence

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.228
metaresearch head score (Gemma)0.415
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2280.415
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0210.021
Science and technology studies0.0030.003
Scholarly communication0.0070.010
Open science0.0040.011
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.710
GPT teacher head0.779
Teacher spread0.069 · 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 designObservational
DomainMethods
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

Citations499
Published2017
Admission routes1
Has abstractyes

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