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Record W4210747336 · doi:10.21203/rs.3.rs-1269601/v1

What implementation strategies and outcome measures are used to transform health care organizations into learning health systems? A mixed methods review protocol

2022· preprint· en· W4210747336 on OpenAlexaff
Mari Somerville, Christine Cassidy, Janet Curran, Melissa Rothfus, Douglas Sinclair, Annette Elliot Rose

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsCINAHLData extractionGrey literatureHealth careContext (archaeology)Critical appraisalInclusion (mineral)ScopusQualitative propertyMEDLINEComputer scienceSystematic reviewProtocol (science)Medical educationKnowledge managementMedicinePsychologyNursingAlternative medicinePsychological interventionPolitical scienceMachine learningSocial psychology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.401
metaresearch head score (Gemma)0.463
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.401
Threshold uncertainty score0.739

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4010.463
Meta-epidemiology (narrow)0.0060.012
Meta-epidemiology (broad)0.0130.020
Bibliometrics0.0210.014
Science and technology studies0.0100.010
Scholarly communication0.0140.011
Open science0.0060.010
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0450.010

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 head0.707
GPT teacher head0.780
Teacher spread0.073 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
Domainnot available
GenreProtocol

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

Citations0
Published2022
Admission routes1
Has abstractno

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