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Record W3006828308 · doi:10.1111/jep.13374

Assessing the quality of seven clinical practice guidelines from four professional regulatory bodies in Quebec: What's the verdict?

2020· article· en· W3006828308 on OpenAlexafffundabout
Gabrielle Ciquier, Michelle Azzi, Catherine Hébert, Kia Watkins‐Martin, Martin Drapeau

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRigourQuality (philosophy)GuidelineTransparency (behavior)Health careClinical PracticeMedicineFamily medicineMedical educationPsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

RATIONALE, AIMS, AND OBJECTIVES: Clinical practice guidelines (CPGs) have become a common feature in the health and social care fields, as they promote evidence-based practice and aim to improve quality of care and patient outcome. However, the benefits of the recommendations reported in CPGs are only as good as the quality of the CPGs themselves. Indeed, rigorous development and strategies for reporting are significant precursors to successful implementation of the recommendations that are proposed. Unfortunately, research has demonstrated that there is much variability in their level of quality. Furthermore, the quality of many CPGs has yet to be examined. The aim of the present study was to assess the quality of seven CPGs from four Quebec professional regulatory bodies pertaining to clinical evaluations in the fields of medicine, psychoeducation, psychotherapy, and social work. METHODS: The seven Quebec CPGs were assessed by four trained appraisers using the Appraisal of Guidelines for Research and Evaluation II guideline evaluation tool. RESULTS: Results suggest that while some quality criteria were met, most were not, denoting that these CPGs are of sub-optimal quality. CONCLUSION: Our findings highlight that there is still a lot to be done in order to improve the rigour and transparency with which scientific evidence is assessed and applied when developing CPGs. Impacts regarding the implementation of these CPGs are discussed in light of their use in clinical practice.

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)0.177
metaresearch head score (Gemma)0.818
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1770.818
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.008
Open science0.0000.000
Research integrity0.0000.004
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 head0.694
GPT teacher head0.695
Teacher spread0.001 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2020
Admission routes3
Has abstractyes

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