THE MEALTIME AUDIT TOOL (MAT) – INTER-RATER RELIABILITY TESTING OF A NOVEL TOOL FOR THE MONITORING AND ASSESSMENT OF FOOD INTAKE BARRIERS IN ACUTE CARE HOSPITAL PATIENTS
Bibliographic record
Abstract
Objectives: Barriers to food intake (FI) exist in hospital that could exacerbate insufficient FI and malnutrition.The Mealtime Audit Tool (MAT) is a staff-administered clinical assessment tool to identify FI barriers for individual patients.Two studies were completed.The objectives of the first study were to test a draft version of the tool and characterize barriers to food intake in older adults in four diverse hospitals, while the second study aimed to demonstrate the inter-rater reliability of the revised MAT.Design: Multi-site, cross sectional.Setting: Four acute care hospitals in Canada.Participants: Study 1: 120 older (65+ years, adequate cognition) medical or surgical patients.Study 2: 90 medical or surgical patients.Measurements: In study 1, participants had barriers experienced at one mealtime assessed with MAT.Descriptive analyses characterized the prevalence of barriers across the hospitals.Revisions were made to the MAT based on recommendations from sites.A revised version was tested for inter-rater reliability in study 2. Intraclass correlation coefficient (ICC) was calculated for total MAT scores from 90 patient meals assessed by two raters.Kappa statistics were calculated for each of the 18 MAT items.Results: Mean (+/-standard deviation) number of barriers experienced in Study 1 was 2.93 +/-1.58, and in Study 2 was 2.51 +/-1.19.The revised MAT was reliable with an ICC of 0.68 (95%CI: 0.52-0.79).Ten of 16 items in which kappa could be calculated had at least fair agreement.Conclusion: MAT is sufficiently reliable when used by auditors with minimal training.Routinely auditing mealtimes with MAT could be useful in identifying and removing barriers to food intake for older hospitalized patients.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".