Evaluation of Food Quality: In Geriatric Institutions
Bibliographic record
Abstract
PURPOSE: The aim was to develop a strategy for evaluating food sensory quality in an institutional setting, the Parameter Specific Sensory Quality (PSSQ) approach, and to compare the inter-evaluator judgement concordance (IEC) using the PSSQ tool versus a traditional tool (TT). METHODS: Inter-evaluator judgement concordance was assessed before and after participants underwent 12 (Study 1) or eight hours of training (Study 2). In Study 1, the IEC was determined before training using the traditional tool only (29 food items) and after training using both the traditional tool and the PSSQ (28 food items). In Study 2, the IEC was determined before and after training using the PSSQ (19 food items). Intraclass correlation coefficients (ICCs) were used to measure the IEC, and data were compared using Fisher's transformation. RESULTS: Study 1 highlighted the poor IEC for the traditional tool in general (ICC(pre)=0.41 vs. ICC(post)=0.43; p>0.1), especially in comparison with that for the PSSQ (ICC(PSSQ)=0.88 vs. ICC(TT)=0.43; p<0.01). Study 2 corroborated the excellent performance of the PSSQ, even when participants had as few as eight hours of training (ICC(post)=0.93). CONCLUSIONS: The inter-evaluator judgement concordance in the evaluation of food sensory quality is fundamental to the generation of valid and useful information. Study results suggest that the food sensory IEC could be improved in hospital settings through the use of a parameter-specific approach, and that this improvement could help ensure the provision of foods of consistent quality.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".