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Record W2140303347 · doi:10.1017/s0266462305290190

Effectiveness and efficiency of guideline dissemination and implementation strategies

2005· article· en· W2140303347 on OpenAlexaff
Jeremy Grimshaw, Ruth Thomas, Graeme MacLennan, Cynthia Fraser, Craig Ramsay, Luke Vale, Paula Whitty, M P Eccles, Lloyd Matowe, Liz Shirran, Michel Wensing, Rob Dijkstra, Cam Donaldson

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2005
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGuidelineResource (disambiguation)DisseminationManagement scienceComputer scienceRisk analysis (engineering)BusinessMedicineEngineering

Abstract

fetched live from OpenAlex

Objectives: A systematic review of the effectiveness and costs of different guideline development, dissemination, and implementation strategies wasundertaken. The resource implications of these strategies was estimated, and a framework for deciding when it is efficient to develop and introduce clinical guidelines was developed.

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)0.207
metaresearch head score (Gemma)0.491
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2070.491
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0110.012
Science and technology studies0.0010.002
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.558
Teacher spread0.520 · 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 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

Citations867
Published2005
Admission routes1
Has abstractyes

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