<i>Food Preferences and Meal Satisfaction</i>of Meals on Wheels Recipients
Bibliographic record
Abstract
PURPOSE: To investigate Edmonton Meals on Wheels (MOW) recipients' food preferences and meal satisfaction. METHODS: A preliminary study of 13 lunch club participants divided into two focus groups was conducted to determine overall themes in clients' menu preferences and suggestions. A questionnaire was developed, based on previous MOW client comments, and delivered to all clients (n=271) receiving hot meal service from the Edmonton MOW program; 140 surveys (52% response rate) were returned. RESULTS: The majority (72% to 88%) of hot meal clients were satisfied with the taste, texture, value, variety, and portion size of their meals. Popular menu items were barbecued chicken, perogies, and desserts. Up to 25% of participants indicated that meats were too tough and vegetables were too firm. Vegetables such as broccoli and Brussels sprouts were the most commonly disliked items. CONCLUSIONS: Overall, clients find the Edmonton MOW menu foods appealing and enjoyable. MOW programs should advertise the availability of texture-modified foods and offer a variety of vegetables. Meal services for the elderly must continue to monitor meal acceptance as client needs change with our aging population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".