Ethical Dimensions of Population-Based Lung Cancer Screening in Canada: Key Informant Qualitative Description Study
Bibliographic record
Abstract
Normative issues associated with the design and implementation of population-based lung cancer screening policies are underexamined. This study was an exposition of the ethical justification for screening and potential ethical issues and their solutions in Canadian jurisdictions. A qualitative description study was conducted. Key informants, defined as policymakers, scientists and clinicians who develop and implement lung cancer screening policies in Canada, were purposively sampled and interviewed using a semi-structured guide informed by population-based disease screening principles and ethical issues in cancer screening. Interview data were analyzed using qualitative content analysis. Fifteen key informants from seven provinces were interviewed. Virtually all justified screening by beneficence, describing that population benefits outweigh individual harms if high-risk people are screened in organized programs according to disease screening principles. Equity of screening access, stigma and lung cancer primary prevention were other ethical issues identified. Key informants prioritized beneficence over concerns for group-level justice issues when making decisions about whether to implement screening policies. This prioritization, though slight, may impede the implementation of screening policies in a way that effectively addresses justice issues, a goal likely to require justice theory and critical interpretation of disease screening principles.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".