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Record W2891254388 · doi:10.1136/bmjgh-2018-000716

Institutionalising an evidence-informed approach to guideline development: progress and challenges at the World Health Organization

2018· article· en· W2891254388 on OpenAlexafffund
Unni Gopinathan, Steven J. Hoffman

Bibliographic record

VenueBMJ Global Health · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsImpactMcMaster UniversityYork University
FundersCanadian Institutes of Health ResearchNorges ForskningsrådGovernment of Ontario
KeywordsGuidelineGrading (engineering)Thematic analysisQualitative researchContext (archaeology)MedicinePublic relationsPsychological interventionPublic healthCredibilityNursingMedical educationPsychologyPolitical scienceSociologyEngineering

Abstract

fetched live from OpenAlex

This study explored experiences, perceptions and views among World Health Organization (WHO) staff about the changes, progress and challenges brought by the guideline development reforms initiated in 2007. Thirty-five semistructured interviews were conducted with senior WHO staff. Sixteen of the interviewees had in-depth experience with WHO's formal guideline development process. Thematic analysis was conducted to identify key themes in the qualitative data, and these were interpreted in the context of the existing literature on WHO's guideline development processes. First, the reforms were seen to have transformed and improved the quality of WHO's guidelines. Second, independent evaluation and feedback by the Guidelines Review Committee (GRC) was described to have strengthened the legitimacy of WHO's recommendations. Third, WHO guideline development processes are not yet designed to systematically make use of all types of research evidence needed to inform decisions about health systems and public health interventions. For example, several interviewees expressed dissatisfaction with the insufficient attention paid to qualitative evidence and evidence from programme experience, and how the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process evaluates the quality of evidence from non-randomised study designs, while others believed that GRADE was just not properly understood or applied. Fourth, some staff advocated for a more centralised quality assurance process covering all outputs from WHO's departments and scientific advisory committees, especially to eliminate strategic efforts aimed at bypassing the GRC's requirements. Overall, the 'culture change' senior WHO staff called for over 10 years ago appears to have gradually spread throughout the organisation. However, at least two major challenges remain: (1) ensuring that all issued advice benefits from independent evaluation, monitoring and feedback for quality and (2) designing guideline development processes to better acquire, assess, adapt and apply the full range of evidence that can inform recommendations on health systems and public health interventions.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.423
GPT teacher head0.582
Teacher spread0.159 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreCommentary

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

Citations14
Published2018
Admission routes2
Has abstractyes

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