The lived experiences of smokers with lung cancer
Bibliographic record
Abstract
Objective: The aims of this research were to learn about the lived experiences of patients with lung cancer who smoke tobacco and to provide nurses with more insights into complexities of people’s relationship with their smoking.Methods: Descriptive phenomenology was used to explore the lived experiences of smokers with lung cancer. An in-depth unstructured conversational style interview was used as a method for data collection. The study was conducted in the inpatient, outpatient, and day care units at the National Center for Cancer Care and Research (NCCCR) in Qatar. Purposive sampling was used to recruit five lung cancer patients who smoke. Colaizzi’s (1978) method was used to analyze data.Results: Participants described three related themes: (a) fate, (b) a socially acceptable addiction, and (c) self-blame and guilt.Conclusions: The findings of this study are of interest to nurses and physicians who work with lung cancer patients. The findings provide insight into experiences of patients who continue to smoke after their lung cancer diagnosis. Nurses within the smoking cessation clinic will also benefit from patients’ descriptions of what they consider useful and supportive in regards to an empathetic, coaching response to their relationships with tobacco. Future study is needed to elucidate nurses' perception on lung cancer patients who continue to smoke.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".