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Record W4285794412 · doi:10.1186/s13012-022-01219-2

The Guideline Language and Format Instrument (GLAFI): development process and international needs assessment survey

2022· article· en· W4285794412 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science · 2022
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsNorth York General HospitalHospital for Sick ChildrenMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersCanadian Thoracic Society
KeywordsMedicineHealth informaticsGuidelineHealth services researchHealth administrationProcess (computing)Public healthMedical educationNursingComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Successful guideline implementation depends both on factors extrinsic to guidelines and their intrinsic features. In the Guideline Implementability for Decision Excellence Model (GUIDE-M), "communicating" content (language and format) is one of three core determinants of intrinsic implementability, but is seldom addressed. Our aims were to develop a tool that could be used by guideline developers to optimize language and format during development; identify gaps in this type of guidance in existing resources; and evaluate the perceived need for and usefulness of such a tool among guideline developers. METHODS: Our mixed-methods design consisted of (1) content development (selection and organization of evidence-based constructs from the GUIDE-M into a prototype Guideline Language and Format Instrument (GLAFI), followed by face validation with guideline developers); (2) document analysis (duplicate) of seven existing guideline tools to measure coverage of GLAFI items and identify new items; and (3) an international survey of guideline developers (corresponding authors of recent Canadian Medical Association or Guidelines International Network database guidelines) to measure perceived importance of language and format, quality of existing resources, and usefulness of a language and format tool. RESULTS: GLAFI items were organized into 4 language and 4 format subdomains. In face validation with guideline developers (17 clinicians, 1 methodologist), all agreed that the tool would improve guideline implementability and 93% indicated a desire for regular use. In the existing guideline tool document analysis, only 14/44 (31.8%) GLAFI items were operationalized in at least one tool. We received survey responses from 148/674 (22.0%) contacted guideline authors representing 45 organizations (9 countries). Language was rated as "extremely important" or "important" in determining uptake by 94% of respondents, and format by 84%. Correspondingly, 72% and 70% indicated that their organization would likely use such a tool. CONCLUSIONS: Optimal language and format are fundamental to guideline implementability but often overlooked. The GLAFI tool operationalizes evidence-based constructs, most of which are absent in existing guideline tools. Guideline developers perceive these concepts to be important and express a willingness to use such a tool. The GLAFI should be further tested and refined with guideline developers and its impact on end-users measured.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.244
GPT teacher head0.585
Teacher spread0.342 · 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