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Record W4400452855 · doi:10.1136/bmjebm-2024-sdc.16

016 Coproduction process of decision aids for colorectal cancer screening in Quebec

2024· article· en· W4400452855 on OpenAlexaffabout
Odilon Quentin Assan, Claude Bernard Uwizeye, Oscar Nduwimana, Hervé TV Zomahoun, Wilhelm Dubuisson, Guillaume Sillon, Danielle Bergeron, Mariejka Beauregard, Camille Poirier-Ouellet, Pamela Bou Malhab, Luc Ricard, Sophie Grignon, Anik Giguère, Marie‐Pierre Gagnon, Stéphane Groulx, Kim Landry-Truchon, Oronzo DE Benedictis, Mélanie Robillard, Wilber Deck, Alan Barkun, Charles Ménard, Jean Y. Dubé, Mélanie Bélanger, France Légaré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcGill UniversityUniversité de SherbrookeCentre Intégré de Santé et Services Sociaux de la GaspésieMinistère de la Santé et des Services Sociaux (Québec)Ministère des TransportsInstitut Universitaire en Santé Mentale de QuébecInstitut National d'Excellence en Santé et en Services SociauxUniversité du QuébecCorporation d’Aménagement et de Protection de la Sainte-Anne
Fundersnot available
KeywordsCoproductionProcess (computing)CancerColorectal cancerComputer scienceMedicinePolitical scienceInternal medicinePublic relations

Abstract

fetched live from OpenAlex

Introduction The Quebec Ministry of Health and Social Services aims to promote shared decision- making (SDM) in population-based colorectal cancer screening. We designed and implemented a Decision Aid (DA) coproduction process with diverse stakeholders. Methods We applied a seven- phase knowledge mobilization approach to guide the process. Phase 1) Setting up the process: Setting up a steering committee (SC), approve protocol in a workshop. • Phase 2) Identify and analyze existing DAs: Conduct systematic review, evaluate and extract DAs content against IPDAS criteria. • Phase 3) Produce the Quebec DA content: Conduct deliberative workshop on existing DAs and generate recommendations for the Quebec DA. • Phase 4) Develop the Quebec French version DA: Updating evidence using rapid reviews, develop DA initial content that is evaluated by the SC through eDelphi process. • Phase 5) Design the Quebec French version DA prototype that is iterated by SC for improvement. • Phase 6) Publish online and translate the French version DA into English. Phase 7) Knowledge transfer: Disseminate DA to the public and train-the-trainers. Results We set up an 18- member SC including citizens; first-line physicians; gastroenterology practitioners and researchers; biochemist; experts in SDM, in DA development and in public health; information specialist and epidemiologists. We identified and analyzed 12 DAs. Based on their strengths, their limitations, and recommendations from SC deliberative workshop, we developed and we published the French version DA on the Quebec Ministry of Health and Social Services website. The English translation process is ongoing. Discussion We have designed a DA coproduction process integrating the user centredness principles. The process is based on best-practice approaches. Conclusion Through this coproduction process, we expect to improve the uptake of the newly developed DA and to improve SDM in population- based colorectal cancer screening in Quebec.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.080
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0090.002
Scholarly communication0.0080.002
Open science0.0040.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0280.003

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 head0.157
GPT teacher head0.531
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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