Identifying the Barriers and Enablers to Nutrition Care in Head and Neck and Esophageal Cancers
Bibliographic record
Abstract
BACKGROUND: The goal of this work was to identify barriers and enablers to the implementation of nutrition care in head and neck and esophageal (HNE) cancers and to prioritize barriers to help improve the nutrition care process. MATERIALS AND METHODS: This study used a multimethod qualitative study design (including semistructured interviews, focus group). Interviews (n = 29) were conducted at 5 European sites providing care and treatment to patients with HNE cancers. A focus group (n = 21) reviewed and corroborated interview findings and identified priorities for nutrition care. Participants were healthcare providers and researchers with direct experience in the field of HNE cancer. RESULTS: Five themes with accompanying barriers and enablers were identified related to nutrition care: (1) evidence for the benefit of nutrition interventions, (2) implementation of nutrition care processes (assessment, intervention, and follow-up), (3) characteristics of healthcare providers, (4) site factors, and (5) patient characteristics. Focus group discussions identified 2 priorities that must be acted on to improve nutrition care: (1) improve the evidence base and (2) develop standardized nutrition care pathways. CONCLUSION: Themes related to nutrition care in HNE cancers were similar between sites, but barriers and enablers differed. Interview and focus group participants agreed the following actions will result in improvements in nutrition care: (1) enhance the evidence base to test the benefit of nutrition interventions, with a focus on resolving specific controversies regarding nutrition therapy, and (2) establish a minimum data set with a goal to create standardized nutrition care pathways where roles and responsibilities for care are clearly defined.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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".