Nutrition Care in Patients With Head and Neck or Esophageal Cancer: The Patient Perspective
Bibliographic record
Abstract
BACKGROUND: Patients with head/neck or esophageal (HNE) cancer are likely to develop malnutrition throughout the course of their disease and its treatment. Although nutrition care is considered a cornerstone of disease management, clinical practices to treat malnutrition vary. The objective of this qualitative study is to understand the patients' experiences with nutrition care in the context of their treatment and recovery. METHODS: A descriptive qualitative study design was used to explore patients' experiences. Ten patients with head and neck (HN) cancer and 10 patients with esophageal cancer were interviewed near the completion of their cancer treatment using a semistructured interview guide. The data sets were analyzed separately using qualitative content analysis. The preliminary findings from each data set were compared and contrasted; 3 themes that crossed both data sets were identified. RESULTS: Three themes were identified: (1) coping with physical and psychosocial aspects of illness and nutrition; (2) understanding the nature of the illness, treatment, and nutrition pathway; and (3) being supported during the trajectory of care. The major differences between HN and esophageal groups were identified in the context of understanding and being supported: the lack of coordination throughout the trajectory of care and conflicting messages from healthcare providers were a source of uncertainty, confusion, and isolation in the HN group. The need for timely and ongoing patient-focused nutrition care, with formal and informal support, was identified in both groups. CONCLUSION: Models for nutrition care should support provision of consistent information across health professionals and throughout the treatment trajectory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".