Dignity and Its Influencing Factors in Patients with Cancer in North China: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Patients with cancer experience various levels of loss of dignity. Exploring levels of loss of dignity and the factors that influence such losses for patients with cancer is rare, but important in palliative care in China. Methods: Participants were cancer patients with early and advanced cancer recruited from a tertiary cancer hospital in North China. Patients were surveyed to assess their level of loss of dignity and potentially relevant factors. Data were collected using the Patient Dignity Inventory, the MD Anderson Symptom Inventory–Chinese, the distress thermometer, the Hospital Anxiety and Depression Scale, and the 30-question core Quality of Life Questionnaire from the European Organisation for Research and Treatment of Cancer, and were analyzed using quantitative methods. Results: The study included 202 cancer patients, 143 of whom experienced mild loss of dignity (71%); 37, moderate loss of dignity (18%); and 10, severe loss of dignity (5%). The problems with dignity were slightly different in patients with early-stage disease than in those with advanced-stage disease. Loss of dignity in the patients was significantly correlated with psychological distress, symptom burden, and quality of life (p < 0.05). Logistic regression showed that age, Karnofsky performance status, anxiety, and symptom burden were significant predictors of loss of dignity. Conclusions: Most patients with early and advanced cancer experienced some level of loss of dignity. Loss of dignity was more likely for patients of younger age, high Karnofsky performance status, high symptom burden, and anxiety. Understanding the dignity of cancer patients and potentially relevant factors is of great value for implementing comprehensive palliative care in China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".