Differences in Lung Cancer Treatment Preferences Among Oncologists, Patients and Family Members: A Semi-Structured Qualitative Study in China
Bibliographic record
Abstract
BACKGROUND: Cancer treatment decision-making often needs to balance benefits, harms, and costs. This study sought to identify the differences in cancer treatment preference among oncologists, patients and their family members in China. METHODS: A semi-structured face-to-face qualitative interview was conducted among oncologists, patients and their family members recruited in four tertiary hospitals in China. The interview guide was developed based on literature review and expert consultation. Participants were asked to indicate their preferences when making lung cancer treatment decisions. All interviews were audio-taped, transcribed verbatim, and thematic analyzed. The preferences were compared among three groups of participants. RESULTS: A total of 17 participants (5 oncologists, 6 dyads of patients and family members) were interviewed between June and July 2019. Five themes, namely, survival benefit, adverse effect/symptom, treatment process, treatment cost, and the impact on daily life were identified. The oncologists and family members gave highest priority on survival benefit, while the patients are concerned most about treatment cost and quality of life. CONCLUSION: This study reveals different preferences for cancer treatment among oncologists, patients and their family members in China. Education is needed to empower patients and family members and promote share decision-making in this country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".