Qualitative research on the perception of benefit in gynecological cancer patients
Bibliographic record
Abstract
Objective: To explore the experience of perceived disease benefit among gynecological cancer patients.Methods: Using purposive sampling, twelve gynecological cancer patients were selected from a tertiary A-level hospital in Wenzhou city, from July to September 2023. Data were collected through face-to-face, semi-structured, in-depth interviews and analyzed using Colaizzi's seven-step method and NVivo 11 software.Results: Five main themes were identified: perception of social support, growth and transformation in mindset, enhancement of health awareness and caregiving ability, gratitude and cherishing life, and improved family relationships.Conclusions: Gynecological cancer patients are able to experience a sense of disease benefit during their treatment. Healthcare professionals should integrate knowledge of positive psychology and communication skills to guide patients in finding positive meanings and help them adopt more proactive coping methods to promote psychophysical health development, thereby improving the quality of life. In addition, it is encouraged that patients' spouses, family members, and friends provide the necessary social support, enhance the level of benefit finding, and establish a robust family and social support system.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".