Recommendations to support nurses and improve the delivery of oncology and palliative care in India
Bibliographic record
Abstract
CONTEXT: Nurses in India often practice in resource-constrained settings and care for cancer patients with high symptom burden yet receive little oncology or palliative care training. AIM: The aim of this study is to explore challenges encountered by nurses in India and offer recommendations to improve the delivery of oncology and palliative care. METHODS: Qualitative ethnography. SETTING: The study was conducted at a government cancer hospital in urban South India. SAMPLE: Thirty-seven oncology/palliative care nurses and 22 others (physicians, social workers, pharmacists, patients/family members) who interact closely with nurses were included in the study. DATA COLLECTION: Data were collected over 9 months (September 2011- June 2012). Key data sources included over 400 hours of participant observation and 54 audio-recorded semi-structured interviews. ANALYSIS: Systematic qualitative analysis of field notes and interview transcripts identified key themes and patterns. RESULTS: Key concerns of nurses included safety related to chemotherapy administration, workload and clerical responsibilities, patients who died on the wards, monitoring family attendants, and lack of supplies. Many participants verbalized distress that they received no formal oncology training. CONCLUSIONS: Recommendations to support nurses in India include: prioritize safety, optimize role of the nurse and explore innovative models of care delivery, empower staff nurses, strengthen nurse leadership, offer relevant educational programs, enhance teamwork, improve cancer pain management, and engage in research and quality improvement projects. Strong institutional commitment and leadership are required to implement interventions to support nurses. Successful interventions must account for existing cultural and professional norms and first address safety needs of nurses. Positive aspects from existing models of care delivery can be adapted and integrated into general nursing practice.
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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 | 0.001 |
| 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".