Global Journal of Medicine & Public Health
Bibliographic record
Abstract
Background Food waste in private hospitals is a significant issue, particularly in South Africa, where food insecurity affected 25.9% of the population. This study investigated food waste management in private hospitals, focusing on its extent, causes, and strategies to mitigate it. Methods A mixed-methods approach was employed, combining quantitative surveys (n=121) and qualitative interviews (n=10) with food service staff and management. Results Food waste in patient meals was prevalent, with 38% of respondents reporting a small extent and 37% a medium extent of waste. At least 62% noted that a quarter of the food served to patients was left uneaten, while 15% reported up to half of meals being wasted. The most common causes of food waste included patients' lack of appetite (27%) and overproduction (22%). Lunch and dinner were identified as the meals with the highest waste levels, with 44.6% and 38.8% of respondents reporting medium levels of waste, respectively. Food distribution systems also played a role, with 77.7% of hospitals using plated meals, which contributed to waste. The study found that varying portion sizes significantly reduced food waste, particularly at dinner (p<0.05). Interviews with hospital managers revealed that digital ordering systems could help reduce food waste, while patient satisfaction was identified as a key factor in minimizing waste. Challenges included staff non-compliance, inconsistent adherence to policies, and health regulations that prevented food redistribution. Conclusion There is a need for improved food management practices, enhanced staff training, and sustainable waste disposal methods to address food waste in private hospitals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.031 |
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".