<i>Assessing the Need for Hot Meals:</i> A Descriptive Meals on Wheels Study
Bibliographic record
Abstract
According to recent literature, delivering chilled Meals on Wheels to seniors increases food quality and safety. The purpose of this study was to determine the acceptability and/or feasibility of a cook-chill delivery system for participants in the Maimonides Geriatric Centre Meals on Wheels program in Montreal, Quebec. The authors also evaluated whether the meal was eaten upon delivery, documented where the meal was stored if consumption was delayed, determined what cooking/heating appliances were used and if the recipients were capable of heating up their meals, and assessed preferences for receiving chilled versus hot meals. Upon receiving the meal, 89% of the 60 seniors did not eat it immediately. Those who ate the meal later stored it in the refrigerator. All had some appliance available to heat the delivered meal; 55% used a microwave. Approximately 75% did not object to receiving meals chilled. The majority of recipients did not require delivery of hot meals, as most delayed consuming the meal until later in the day. Other meal-delivery program planners can use these findings when deciding if a cook-chill system is appropriate for their client populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".