An Integrative Review to Examine the Care Pathways and Support Available for Individuals Diagnosed with Lung Cancer Who Have Never Smoked
Bibliographic record
Abstract
As the proportion of people diagnosed with lung cancer who have never smoked rises, it is important to understand how their experiences differ from those of smokers. A better understanding of their experiences, views, and informational and supportive care needs is essential to ensuring an optimised patient-centred care pathway leading to improvements in patient satisfaction, quality of life, and treatment outcomes. This integrative review of the international literature identified 5866 articles by searching four academic databases, the grey literature, and hand-searching the reference lists of relevant systematic reviews. After screening, ten studies were selected for inclusion in the review. Thematic analysis identified five themes that spoke to the experiences of never smokers with lung cancer: stigma, awareness, diagnosis, the emotional response, and support. Stigma pervades, with potentially significant psychological and social consequences, negatively affecting patients emotionally and potentially delaying their diagnosis. Increasing awareness amongst healthcare professionals and the general public has the potential to reduce stigma and encourage earlier diagnosis. Support specifically tailored for never smokers with lung cancer can improve individuals' experiences of care. The experiences of never smokers with lung cancer are unique, and more research is required to better tailor support and guidance for this cohort.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".