The Role of Registered Dietitians in Cancer Palliative Care: Responsibilities, Challenges, and Interdisciplinary Collaboration—A Cross-Sectional Survey
Bibliographic record
Abstract
Registered dietitians (RDs) in palliative care help maintain patients’ quality of life by providing personalized nutritional support that alleviates eating-related distress. This study aimed to clarify the role of RDs in palliative care by examining their responsibilities and challenges in caring for cancer patients. A nationwide mailed survey was conducted in 2022, focusing on RDs involved in cancer palliative care. One RD per facility was included from all 501 hospitals accredited by Japan’s Ministry of Health, Labour and Welfare. Multivariate analysis identified factors related to collaboration with palliative care teams and challenges in cancer care. Responses from 325 RDs (63.9%) across 325 hospitals (63.9%) were analyzed. Among RDs who consistently collaborated with the palliative care team (PCT), significant associations (p < 0.05) were found with exclusive engagement in cancer/palliative care, providing nutritional counseling to inpatients, the frequency of ward rounds, and individualized meal provision. Challenges included the following: “I struggled with determining appropriate food choices for patients unable to eat”, and “Metabolic complications like cachexia hindered my ability to provide adequate support”. RDs play a crucial role in providing individualized meals for cancer patients through PCT collaboration and ward rounds. To ensure effective support in challenging situations, RDs must be exclusively engaged in palliative care and receive specialized education.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".