Ethical dimensions of lung cancer screening in Canada
Bibliographic record
Abstract
Background and aim: Lung cancer is the leading cause of cancer incidence and mortality in Canada. Population-based screening programs using low dose computed tomography are being more widely used. Screening reduces lung cancer mortality. It also introduces potential ethical issues that need to be elucidated to inform the ethical, equitable, and effective implementation of screening programs. This aim of this research was to begin developing an understanding of what the ethical issues are and how they are being, and should be, approached in health policy. Methods: Using empirical ethics inquiry, this research produced descriptive evidence via three independent studies: a systematic literature review and mixed methods integrative synthesis of public perspectives on screening benefits and harms, and two qualitative description studies about public and key informants’ ethical and social values on ethical issues in screening. Results: The major finding of this research was the preponderance of ethical issues located within health and social systems and structures, including equity of screening access, stigma against people who currently smoke commercial tobacco, commercialization of tobacco, and the need for increased investment in primary prevention of lung cancer. These ethical issues reflect the social, economic, and political determinants of lung cancer and the means available to reduce the burden of lung cancer in Canada, including but not limited to screening. In health policy, there was a lack of ethical frameworks or principles currently being used to address these ethical issues and the sometimes-conflicting perspectives found between the public and key informants. Discussion: Future empirical and normative research is needed to understand ethical and social values related to screening by populations with high lung cancer incidence and mortality, and to integrate empirical evidence with appropriate ethical theories to make recommendations for ethical, equitable, and effective population-based LDCT lung cancer screening policy in Canada.
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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 | 0.037 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.038 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".