Ethical Dimensions of Population-Based Lung Cancer Screening in Canada: Key Informant Qualitative Description Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it