Emotions and lung cancer screening: Prioritising a humanistic approach to care
Bibliographic record
Abstract
Low-dose computed tomography lung cancer screening has mortality benefits. Yet, uptake has been low. To inform strategies to better deliver and promote screening, in 2018, we interviewed 27 long-term smokers immediately following lung cancer screening in Australia, prior to receiving scan results. Existing lung screening studies employ the Health Belief Model. Reflecting growing acknowledgement of the centrality of emotions to screening uptake, we draw on psychological and sociological theories on emotions to thematically and abductively analyse the emotional dimensions of lung cancer screening, with implications for screening promotion and delivery. As smokers, interviewees described feeling stigmatised, with female participants internalising and male participants resisting stigma. Guilt and fear related to lung cancer were described as screening motivators. The screening itself elicited mild positive emotions. Notably, interviewees expressed gratitude for the care implicitly shown through lung screening to smokers. More than individual risk assessment, findings suggest lung screening campaigns should prioritise emotions. Peer workers have been found to increase cancer screening uptake in marginalised communities, however the risk to confidentiality-especially for female smokers-limits its feasibility in lung cancer screening. Instead, we suggest involving peer consultants in developing targeted screening strategies that foreground emotions. Furthermore, findings suggest prioritising humanistic care in lung screening delivery. Such an approach may be especially important for smokers from low socioeconomic backgrounds, who perceive lung cancer screening and smoking as sources of stigma and face a higher risk of dying from lung cancer and lower engagement with screening.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".