What Information on Mammographic Screening is Available to Women in Quebec, Ontario and Canada: Results from a Document Analysis
Bibliographic record
Abstract
Information provided to women invited to participate in mammography screening is crucial in supporting informed consent. Expert recommendations remain divided, and the ethical framing of autonomy, especially in shared decision-making, often fails to reflect the realities of clinical practice. This study examines how public-facing documents communicate screening information and explores whether relational autonomy offers a more ethically coherent approach. A document analysis was conducted using the READ method. Documents published between 1999 and 2023, from Quebec, Ontario or Canada, containing the keywords 'breast cancer screening' and 'screening mammography' were included and assessed using a 16-category grid. Fifty-one documents were included (Qc: 16, On: 10, Can: 25) and 11 were excluded. 10 per cent included women from the public, but the majority of contributing experts were women (57 per cent). 96 per cent of documents were considered inaccessible based on the Flesch-Kincaid Grade Level score. Benefits were mentioned more often than risks (90 per cent vs 37 per cent) and 16 per cent confused diagnosis and screening. Women under 50 were overrepresented (37 per cent) and racialized women underrepresented (58 per cent). This analysis reveals biases in how screening information is designed and communicated. Relational autonomy offers a more inclusive framework for evaluating and improving screening communication.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".