Investigating the patient food experience: Understanding hospital staffs' perspectives on what leads to quality food provision in Ontario hospitals
Bibliographic record
Abstract
BACKGROUND: Food quality influences patient food satisfaction, which may subsequently affect food intake and recovery, influencing hospital costs. The present qualitative study aimed to gain an understanding of hospital staff/volunteers experiences of serving food in Ontario hospitals, perceptions of food quality and challenges to quality food provision. METHODS: Sixteen Ontario hospitals participated. Semi-structured interviews (n = 64 participants) and focus groups (n = 24; 150 participants) were conducted. Transcripts were analysed using inductive thematic analysis. RESULTS: Four themes emerged: (1) Providing Good Quality Food (e.g., attributes that comprise the construct of meal quality, patients' expectations and desires from meals); (2) Individualising the Food and Mealtime Experience (e.g., processes to identify and cater to patients' needs and preferences); (3) Acknowledging Organisational Constraints (e.g., staffing, budget, etc.); and (4) Innovating Beyond Constraints (e.g., identifying innovation within potential modifiable and unmodifiable organisational constraints). CONCLUSIONS: Serving meals in hospital is complex because of organisational and patient factors; however, current efforts to serve quality food despite these complexities were uncovered in our investigation. Discussions highlighted current practices that promote food quality and strategies for improvement. Improving food quality and the hospital meal experience can support food intake and patient outcomes, as well as reduce waste and hospital associated costs. The findings can be used to support quality improvement measures aiming to serve high quality food that meets patients' expectations and nutritional needs.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".