Coproduction process of decision aids for population-based cancer screening in Quebec
Bibliographic record
Abstract
Context. As healthcare and social services systems are called on to be more learning, more resilient, more efficient and more in partnership with all stakeholders, it becomes critical to apply shared decision-making (SDM). Trends show increasing cancer incidence in Quebec. The Quebec Ministry of Health and Social Services aims to promote SDM in population-based cancer screening. In 2019, they engaged our team to provide Decision Aids (DAs) for cancer screening to be available throughout Quebec’s publically-funded healthcare system. We then codesigned and implemented a DA coproduction process with different stakeholders. Objective. To describe our DA coproduction process. Study Design and Analysis. We applied a seven-phase knowledge mobilization approach to guide the process : Phase 1. Set 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. Generate DA content: Conduct deliberative workshop on existing DAs and generate recommendations for the Quebec DA, Phase 4. Develop the content of the French DA: Updating evidence using rapid reviews, develop DA initial content that is evaluated by the SC through eDelphi process, Phase 5. Design the prototype of the French DA, Phase 6. Deploy the French DA and the English DA, Phase 7. Operate the knowledge translation: Disseminate DA to the public and to health professionals. Results. We set up around 20-member SC to implement and guide this process. It includes citizens, first-line physicians, gastroenterology practitioners and researchers, biochemist, experts in SDM, in DA development and in public health, information specialist and epidemiologists. We coproduced and published on the Quebec Ministry of Health and Social Services website, French and English DA for lung and colorectal cancer screenings. They are under use in the population-based screening activities and are subject to knowledge transfer activities including integration into best practice guides, screening promotion video-clips for the general public, train-the-trainer activities and dissemination to the public through conferences. The coproduction of the DA for cervical cancer screening is in progress. Conclusions. Through this process, we expect to improve the uptake of the newly developed DA and to improve SDM in population-based colorectal cancer screening in Quebec.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.122 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".