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Record W2770333214 · doi:10.1186/s13012-017-0668-7

Number and type of guideline implementation tools varies by guideline, clinical condition, country of origin, and type of developer organization: content analysis of guidelines

2017· article· en· W2770333214 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.

Bibliographic record

VenueImplementation Science · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineGuidelineHealth informaticsMEDLINEFamily medicinePublic healthNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Guideline implementation tools (GI tools) can improve clinician behavior and patient outcomes. Analyses of guidelines published before 2010 found that many did not offer GI tools. Since 2010 standards, frameworks and instructions for GI tools have emerged. This study analyzed the number and types of GI tools offered by guidelines published in 2010 or later. METHODS: Content analysis and a published GI tool framework were used to categorize GI tools by condition, country, and type of organization. English-language guidelines on arthritis, asthma, colorectal cancer, depression, diabetes, heart failure, and stroke management were identified in the National Guideline Clearinghouse. Screening and data extraction were in triplicate. Findings were reported with summary statistics. RESULTS: Eighty-five (67.5%) of 126 eligible guidelines published between 2010 and 2017 offered one or more of a total of 464 GI tools. The mean number of GI tools per guideline was 5.5 (median 4.0, range 1 to 28) and increased over time. The majority of GI tools were for clinicians (239, 51.5%), few were for patients (113, 24.4%), and fewer still were to support implementation (66, 14.3%) or evaluation (46, 9.9%). Most clinician GI tools were guideline summaries (116, 48.5%), and most patient GI tools were condition-specific information (92, 81.4%). Government agencies (patient 23.5%, clinician 28.9%, implementation 24.1%, evaluation 23.5%) and developers in the UK (patient 18.5%, clinician 25.2%, implementation 27.2%, evaluation 29.1%) were more likely to generate guidelines that offered all four types of GI tools. Professional societies were more likely to generate guidelines that included clinician GI tools. CONCLUSIONS: Many guidelines do not include any GI tools, or a variety of GI tools for different stakeholders that may be more likely to prompt guideline uptake (point-of-care forms or checklists for clinicians, decision-making or self-management tools for patients, implementation and evaluation tools for managers and policy-makers). While this may vary by country and type of organization, and suggests that developers could improve the range of GI tools they develop, further research is needed to identify determinants and potential solutions. Research is also needed to examine the cost-effectiveness of various types of GI tools so that developers know where to direct their efforts and scarce resources.

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)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.523
GPT teacher head0.637
Teacher spread0.114 · 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