Nutrition education: Optimising preparation and recovery for benign oesophageal surgery
Bibliographic record
Abstract
BACKGROUND: Patients requiring upper gastrointestinal surgery for benign oesophageal conditions are at nutrition risk before and after surgery. There is a dearth of published evidence guiding clinicians on effective collaboration with patients to mitigate perioperative nutritional challenges. We conducted a qualitative study aiming to explore patients' perioperative food, nutrition, and educational experiences to guide future care. METHODS: Adult patients who had undergone elective, benign oesophageal surgery were invited to participate in semi-structured interviews within 3 weeks of hospital discharge. Interviews were transcribed and analysed with a reflexive form of inductive thematic analysis in addition to synthesised member checking. RESULTS: Interviews with 12 patients identified three major themes. First, nutrition education fosters a better surgical recovery experience: patients expressed a desire to be prepared for their upcoming surgery and engage in the recovery process with informed food choices. Most patients preferred preoperative education given limited capacity for learning during hospital admission. Second, patients have priorities for nutrition information: patients expressed that educational material should be printed, comprehensive, practical, include familiar foods and focus on managing postoperative physical symptoms. Third, food impacts social and emotional experiences of surgery: resumption of a normal diet was a sign of recovery that enabled social reintegration. Identified themes resonated with Knowles' six-core principles of andragogy. CONCLUSIONS: Patients with benign oesophageal conditions perceived nutrition education to be a vital aspect of surgical preparation and recovery. Re-designing perioperative education with patient input has the potential to improve outcomes and experiences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".