Process for Rapid Co-development of a Decision Aid Prototype for Population-wide Cancer Screening
Bibliographic record
Abstract
Decision aids (DA) are more likely to be adopted if co-developed with stakeholders and culturally adapted. Using the DEVELOPTOOLS Reporting Checklist, we describe a process for rapid co-development of a culturally adapted DA prototype for population-wide cancer-screening programs. Our systematic, collaborative, and iterative methodology had 7 phases: 1) set up the process by adopting best governance practices (e.g., identify and engage stakeholders, adapt our collaborative DA design process, validate development process), with governance comprising 20 individuals from a wide range of sectors including at least 2 citizens; 2) identify and analyze existing DAs relevant to the cancerscreening of interest by conducting a systematic review; 3) share results with stakeholders and make recommendations; 4) formulate Quebec-specific DA content and consult stakeholders including users by conducting e-Delphi surveys; 5) co-design a prototype with stakeholders, including users, following international DA standards; 6) translate the DA using translation-back translation approaches and deploy; and 7) knowledge mobilization (KMb) using end-of-grant and integrated KMb activities. Using the User-Centred Design 11-Item Measure (UCD-11), our proposed process scored 10 of 11 on the UCD-11. Overall, we expect this new co-developed process to ensure that good-quality, user-centered, and culturally adapted DAs for cancer screening are produced within reasonable timeframes. We also expect it to foster the adoption of the DAs.HighlightsWe report on a 7-step process for collaborating with various stakeholders to create a culturally adapted decision aid (DA) prototype for deciding about cancer screening in Quebec, Canada.The process includes: ○ Making sure the DA prototype design includes users and other interested parties and reflects their needs, perceptions, values, and preferences.○ Finding and analyzing existing DAs on cancer screening to decide what ours should include○ Respecting international standards and criteria for DA design○ Repeated rounds of expert consensus about the exact content, with revisions between each roundThis method could help the rapid creation of DAs shaped by users' interests and will ultimately encourage shared decision making.
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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 | 0.183 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".