Public perspectives on ethical issues in lung cancer screening policy design and implementation in Ontario, Canada
Bibliographic record
Abstract
• Public views on cancer screening ethical issues may contribute to policymaking. • Screening programs are being implemented to reduce lung cancer mortality. • This research examined public views on lung cancer screening ethical issues. • Participants supported high-risk screening, except for people who currently smoke. • Screening policies should more effectively mitigate smoking stigma. Public perspectives on ethical issues in cancer screening may contribute to informing policymaking. Lung cancer screening is being implemented with the aim of reducing lung cancer mortality. Inequitable lung carcinogen exposure and lung cancer disparities are key ethical challenges in screening. This research aimed to examine public perspectives about ethical issues in lung cancer screening. A qualitative description study was conducted in Ontario, Canada, where a provincial lung cancer screening program is being implemented. Using maximum variation sampling, Ontario residents aged 55–85 years were recruited via family medicine clinics, social media, and personal networks. Semi-structured interviews were conducted with individual participants to elicit their perspectives on established ethical issues in cancer screening, with questions focused on potential lung cancer screening benefits and harms, who should be eligible, and why. Twenty-six individuals participated in this study. Participants were aged 61−70 years and of various education levels. Sixty-five percent were women. No participants currently smoked commercial tobacco. Participants believed screening was important for reducing lung cancer mortality and saving healthcare costs. Participants stated that screening should consider and prioritize a wider range of lung cancer risk factors, such as occupational exposures and family history of lung cancer, than factors currently being used to offer screening to those at high risk. Participants gave less priority to screening for people who currently smoke. Public perspectives supported screening high-risk candidates; however, support may be undermined by smoking stigma. Screening policies should more effectively mitigate stigma and ethically justify screening candidacy decisions.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| 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".