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Record W3194871931 · doi:10.11124/jbies-21-00064

Trends in guideline implementation: an updated scoping review protocol

2021· article· en· W3194871931 on OpenAlexaff
Anna R. Gagliardi, Jennifer Malinowski, Zachary Munn, Sanne Peters, Emily Senerth

Bibliographic record

VenueJBI Evidence Synthesis · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsCINAHLPsychological interventionGuidelineMEDLINEScopusMedicineProtocol (science)Cochrane LibraryStakeholderIntervention (counseling)ChecklistHealth careGrey literatureSystematic reviewMedical educationNursingAlternative medicinePsychologyPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this review is to assess trends in guideline implementation, including the interventions used, rationale provided, and the impact on patient or health care professional knowledge, behavior or outcomes. INTRODUCTION: Guidelines must be actively implemented to promote use and achieve beneficial outcomes. A review published in 2015 found that studies of guideline implementation did not employ a range of implementation planning approaches to select and tailor interventions, resulting in inconsistent impact. This study will update the 2015 review and elaborate beyond the four diseases originally covered to determine whether more recent efforts to implement guidelines are informed by best implementation practices. INCLUSION CRITERIA: We will include published studies that describe the implementation of guidelines on any clinical topic relevant to primary, secondary, or tertiary care using interventions targeted at patients, families/caregivers, or health care professionals. METHODS: We will search MEDLINE, Embase, AMED, CINAHL, Scopus, and the Cochrane Library from 2014 (search date in 2015 review) to the present. Two or more reviewers will screen titles and full-text articles, and extract data from included studies. We will use summary statistics, tables, and a narrative summary to describe study characteristics, guideline implementation interventions, the rationale for intervention selection and tailoring (pre-identified barriers, patient or stakeholder preferences, theory), and intervention impact.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.342
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1200.000

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 head0.418
GPT teacher head0.705
Teacher spread0.287 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations5
Published2021
Admission routes1
Has abstractyes

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