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Record W2112958466 · doi:10.9778/cmajo.20140064

The development of guideline implementation tools: a qualitative study

2015· article· en· W2112958466 on OpenAlex
Anna R. Gagliardi, Melissa Brouwers, Onil Bhattacharyya

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCMAJ Open · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWomen's College HospitalMcMaster UniversityUniversity Health Network
FundersUniversity Health Network
KeywordsGuidelineProcess (computing)Process managementQualitative researchComputer scienceKnowledge managementMedical educationMedicineEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Research shows that guidelines featuring implementation tools (GItools) are more likely to be used than those without GItools, however few guidelines offer GItools and guidance on developing GItools is lacking. The objective of this study was to identify common processes and considerations for developing GItools. METHODS: Interviews were conducted with developers of 4 types of GItools (implementation, patient engagement, point-of-care decision-making and evaluation) accompanying guidelines on various topics created in 2008 or later identified in the National Guideline Clearinghouse. Participants were asked to describe the GItool development process and related considerations. A descriptive qualitative approach was used to collect and analyze data. RESULTS: Interviews were conducted with 26 GItool developers in 9 countries. Participants largely agreed on 11 broad steps, each with several tasks and considerations. Response variations identified issues lacking uniform approaches that may require further research including timing of GItool development relative to guideline development; decisions about GItool type, format and content; and whether and how to engage stakeholders. Although developers possessed few dedicated resources, they relied on partnerships to develop, implement and evaluate GItools. INTERPRETATION: GItool developers employed fairly uniform and rigorous processes for developing GItools. By supporting GItool development, the GItool methods identified here may improve guideline implementation and use.

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.

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)0.023
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.900
GPT teacher head0.799
Teacher spread0.101 · 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