Exploring the Experiences of Individuals Diagnosed with Metastatic Non-Small-Cell Lung Cancer: A Qualitative Study
Bibliographic record
Abstract
Advancements in targeted therapies and immunotherapies have improved survival for individuals with metastatic non-small-cell lung cancer (mNSCLC), creating a growing population of Canadians living long-term with the disease. These individuals face ongoing physical, emotional, and practical challenges, yet existing supportive care services are often designed for patients receiving curative intent treatment and may not adequately address the challenges of those undergoing continuous treatment. To explore these experiences and inform the development of supports tailored to their needs, eight participants with mNSCLC completed one-on-one virtual interviews. They described limited support for managing side effects and psychosocial concerns despite general satisfaction with oncology care. Fatigue and cognitive challenges impacted daily functioning, and emotional challenges (e.g., fear of progression, stigma, and difficulty finding meaning) impacted quality of life. Financial burden, including unexpected costs and loss of income, further affected their well-being. Existing supports, such as exercise programs, were viewed positively but were often difficult to access, were offered only short-term, and required patients to find them independently. Recommendations included improved coordination and communication across the healthcare system, alongside tailored interventions such as navigation services, resource directories, health promotion supports, and expanded peer support. Overall, people living long term with mNSCLC face distinct challenges and unmet supportive care needs, highlighting the importance of integrating supportive services into routine oncology care.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".