Food Is Medicine: A Qualitative Analysis of Patient and Institutional Barriers to Successful Surgical Nutrition Practices in an Enhanced Recovery After Surgery Setting
Bibliographic record
Abstract
BACKGROUND: Close adherence to the Enhanced Recovery After Surgery (ERAS) program is associated with improved outcomes. A nutrition-focused qualitative analysis of patient experience and of ERAS implementation across our province was conducted to better understand the barriers to successful adoption of ERAS nutrition elements. METHODS: Enrolled colorectal patients (n = 27) were asked to describe their surgical experience. Narrative interviews (n = 20) and focus groups (n = 7) were transcribed verbatim and analyzed inductively for food and nutrition themes. Qualitative data sources (n = 198 documents) collected throughout our implementation of ERAS were categorized as institutional barriers that impeded the successful adoption of ERAS nutrition practices. RESULTS: We identified patient barriers related to 3 main themes. The first theme, Mistaken nutrition facts & beliefs, describes how information provision was a key barrier to the successful adoption of nutrition elements. Patients held misconceptions and providers tended to provide them with contradictory nutrition messages, ultimately impeding adequate food intake and adherence to ERAS elements. The second theme, White bread is good for the soul?, represents a mismatch between prescribed medical diets and patient priorities. The third theme, Food is medicine, details patient beliefs that food is healing; the perception that nutritious food and dietary support was lacking produced dissatisfaction among patients. Overall, the most important institutional barrier limiting successful adoption of nutrition practices was the lack of education for patients and providers. CONCLUSION: Applying a patient-centered model of care that focuses on personalizing the ERAS nutrition elements might be a useful strategy to improve patient satisfaction, encourage food intake, correct previously held beliefs, and promote care adherence.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".