An Exploration of the Challenges for Oncology Nurses in Providing Hospice Care in Mainland China: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: Although there has been an increasing emphasis on assisting nurses with providing quality hospice care to patients and family members, few studies have explored the challenges that oncology nurses face when delivering hospice care in the Chinese cultural context. The objective of this study was to elucidate the challenges for oncology nurses in providing hospice care for terminally ill cancer patients in mainland China. METHODS: A descriptive qualitative study with purposive sampling using audio-recorded fact-to-face interviews. A total of 13 hospice nurses from four hospitals in Beijing, mainland China, participated in this study. Data collection was from April to June 2019, and thematic analysis method was used to analyze the data. RESULTS: Challenges identified by hospice nurses in providing hospice care for terminally ill cancer patients included: (1) public misperception on hospice care, (2) lack of financial support, (3) fear of medical disputes and legal action, (4) shortage of human resources, (5) insufficient specialization and lack of "hierarchy" training on hospice care, (6) inexperience in communication skills, and (7) lack of self-care and stress management skills. CONCLUSIONS: It is imperative and critical for the government, health-care institutions, and hospice care providers to clearly understand the challenges that currently exist in providing hospice nursing. Joint efforts are needed to overcome those challenges, which might result in qualified hospice nurses and provide evidence for further development of hospice care in mainland 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 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) | 0.002 | 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".