Public perspectives on the benefits and harms of lung cancer screening: A systematic review and mixed-method integrative synthesis
Bibliographic record
Abstract
ObjectiveScreening for lung cancer with low dose computed tomography aims to reduce lung cancer mortality, but there is a lack of knowledge about how target populations consider its potential benefits and harms.MethodsWe conducted a systematic review of primary empirical studies published in any jurisdiction since 2002 using an integrative meta-synthesis technique. We searched six health and social science databases. Two reviewers independently screened titles, abstracts, and potentially eligible full-text studies. Quantitative assessments and open-ended perspectives on benefits and harms were extracted and convergently integrated at analysis using a narrative approach. Study quality was assessed.ResultsThe review included 26 quantitative, 18 qualitative, and 5 mixed methods studies. Study quality was acceptable. Lung cancer screening was widely perceived to be personally beneficial for early detection and reassurance. Radiation exposure and screening accuracy were recognised as harms, but these were frequently considered to be justified by early detection of lung cancer. Stigma, anxiety, and fear related to screening procedures and results were pervasive among current smokers. People with low incomes reported not participating in screening because of potential out-of-pocket costs and geographic access.ConclusionsPopulations targeted for lung cancer screening tended to consider screening as personally beneficial and rationalised physical, but not psychological, harms. Screening programmes should be clear about benefits, use non-stigmatising design, and consider equity as a guiding principle.
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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 | 0.075 | 0.164 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".