<i>Transformation to Room Service Food Delivery</i> In a Pediatric Health Care Facility
Bibliographic record
Abstract
Patient food service is an important component in the nutritional management of hospitalized children. The previous meal delivery system at The Hospital for Sick Children in Toronto was a cold-plating re-thermalized system. Issues related to this model included order lead time, the reheating process, menu selection, and service style. Research into other systems led us toward room service, an innovative and flexible mode of meal delivery. Transformation to room service occurred over one year, and included implementation of a new computer system, kitchen renovation, redesign of menus and a new meal delivery system called Meal Train, and changes to human resource allocations. Throughout the transformation, consultations were held with key stakeholders, including the children's council, the family advisory, the nursing council, and a multidisciplinary committee involving nursing staff, dietitians, patient service aides, infection control personnel, occupational health employees, patient representatives, and food services staff. Now, Meal Train is running smoothly, and meal days and food costs have been reduced. Others considering a project like this must know their clients' needs and be willing to think outside the box. They should familiarize themselves with current information on systems and equipment, consult with key stakeholders within their organization, and then create the system that will work for them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".