Supporting women at average risk to make informed decisions about mammography when there is no “right” answer: a qualitative citizen deliberation study
Bibliographic record
Abstract
BACKGROUND: Women are encouraged to make informed choices about mammography screening that align with their values and preferences, yet information materials developed by screening programs rarely provide complete, balanced information about screening. Through a series of deliberations with Ontario citizens, we elicited perspectives on materials developed by screening programs to support informed decision-making. METHODS: We held 4 deliberative engagement events with citizens to discuss the current evidence about mammography and informed decision-making for the general population (i.e., women not at high risk) in the context of organized screening programs. Participants reviewed and provided feedback on the educational materials currently produced by screening programs in 8 provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, Nova Scotia and Newfoundland and Labrador) and 2 territories (Yukon Territory and Northwest Territory) and identified the key features that should guide the design of these materials to optimally support informed decision-making. RESULTS: In general, participants viewed the educational materials as insufficient to support informed decision-making. They identified the following key features of optimal educational materials: they should be accessible, complete and accurate, and provide information on both benefits and risks of screening in a comprehensive, easy-to-understand manner. Information materials should evoke the trust of the reader, and they should be consistent across Canada. INTERPRETATION: Canadian women have insufficient access to reliable information sources and complete evidence about mammography screening, and, without this information, they are unable to make fully informed decisions. Canadian breast screening programs must take steps to improve the information shared with women to support informed decision-making that aligns with women's values and preferences.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".