Exploring Experiences of Survivors and Caregivers Regarding Lung Cancer Diagnosis, Treatment, and Survivorship
Bibliographic record
Abstract
BACKGROUND: Advances in screening and treatment approaches alongside changing population demographics have the potential to influence the experience of living with lung cancer. There is potential for improved outcomes and quality of life for those diagnosed with the disease. OBJECTIVES: This exploratory study was undertaken to gain insight regarding the current experiences of individuals diagnosed with lung cancer and their family caregivers given the evolving changes in lung cancer screening and treatment. METHOD: A qualitative descriptive design was utilized and in-depth interviews conducted with 8 survivor and 4 family caregivers. Interviews were subjected to a conventional content analysis. RESULTS: Participants identified challenges related to being diagnosed in a timely manner, being told the diagnosis with compassion, coping with multiple symptoms during treatment, and regaining a new normal following treatment. Dealing with late effects of treatment (ie, fatigue, shortness of breath, neuropathy) was frustrating when individuals were not aware the effects would emerge or had not had relevant self-management instructions. CONCLUSIONS: Lung cancer survivors constitute an emerging cadre of survivors. Attention is needed to their preparation for, and coping with, the survivorship transition.
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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 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".