Investigating food waste generation at long-term care facilities in Ontario
Bibliographic record
Abstract
Purpose This research aims to investigate the sources of food waste generation at two long-term care (LTC) homes located in Canada. Given the distinctive regulatory and operational context LTC's work within there is an opportunity that unique causes of food waste exist. Design/methodology/approach An initial audit using the Food Delivery System Framework determined the most appropriate method to measure food waste for this study was a quantitative approach supported by field observations. Findings Results of the study show a significant food waste of over 55% at both facilities. Investigation into the generation of this waste isolated the major cause being government policy ensuring patients at these facilities are offered choices throughout the meal selection process. Plate waste was generated because of additional policies guaranteeing pre-determined nutritional and caloric intakes for each patient. Practical implications These findings put into question the operating practices involved with adhering to policies on the choice of food. Ethical questions are raised pitting a patient's “quality of life” versus the environmental impact of the waste generated because of policies. Field observations note a “throw away culture” and an absence of established foodservice management practices that create a lack of awareness and attention to the issue of food waste. Originality/value Most studies of food waste in healthcare facilities have looked at caloric and nutritional loss from a patient's point of view. To the best of the authors’ knowledge, this study is one of the first to look at the causes of food waste generation in these operations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".