016 Coproduction process of decision aids for colorectal cancer screening in Quebec
Bibliographic record
Abstract
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.
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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 | 0.080 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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.
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".