Ethical Dilemmas Among Oncology Nurses in China: Cross-Sectional Study
Bibliographic record
Abstract
Background: Effective communication about cancer prognosis is imperative for enhancing the quality of end-of-life care and improving patient well-being. This practice is sensitive and is heavily influenced by cultural values, beliefs, and norms, which can lead to ethical dilemmas. Despite their significance, ethical challenges in nursing related to prognosis communication are understudied in China. Objective: This study aimed to examine the ethical dilemmas relating to cancer prognosis communication and their associated factors. Methods: A cross-sectional design was employed to survey 373 oncology nurses in mainland China. Data were collected on ethical dilemmas, attitudes, barriers, experiences with prognosis communication, sociodemographics, and practice-related information. Ordinary least squares regressions were used to identify factors contributing to ethical dilemmas. Results: Participants reported a moderate level of ethical dilemmas in prognostic communication (mean 13.5, SD 3.42; range 5-20). Significant predictors of these dilemmas included perceived barriers (P<.001), experiences with prognosis communication (P<.001), and years of work experience (P=.002). Nurses who perceived greater communication barriers, had more negative experiences with prognosis communication, and had less work experience were more likely to encounter ethical dilemmas in prognosis-related communication. Conclusions: Chinese oncology nurses frequently encounter ethical dilemmas, as well as barriers, in communicating cancer prognoses. This study's findings emphasize the importance of culturally tailored communication training. Collaborative interprofessional training, particularly through physician-nurse partnerships, can perhaps enhance the proficiency of cancer prognosis-related communication.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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".