Inter-Rater Reliability of the Mealtime Scan+
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Mealtime Scan (MTS) was developed to assess the dining environment in Long Term Care (LTC). MTS has been reviewed and updated to ensure its standardization and responsiveness to changes in the dining environment. The objectives of this paper are to provide an overview of the changes made in the new MTS+ tool and to describe its inter-rater reliability. RESEARCH DESIGN AND METHODS: The observational and scoring methods used to evaluate the physical, social, and relational-centered dining environments in LTC homes were reviewed and updated into MTS+ and an overall quality of dining rating derived. Two trained assessors were evaluated for the inter-rater reliability. Nine dining rooms in three LTC homes at different meals were observed for a total of 45 observations. Interclass Correlation Coefficient (ICC) was used to assess reliability. RESULTS: MTS+ demonstrated good reliability on the orientation cues, social environment, and relationship-centered activities (orientation cues ICC: 0.75, social ICC: 0.78, relational ICC: 0.78). However, the tool's reliability was poor for the physical environment, ICC: 0.48 and moderate for the overall rating of the quality of the dining environment, ICC: 0.67. Discussion and Implications: Although deemed more effective and practical for use in intervention studies, MTS+ has some limitations with respect to its reliability, particularly in assessing the physical environment. It is hypothesized that more extensive training of the assessors may lead to improvements in the reliability of MTS+. MTS+ can be used in intervention research when one assessor completes repeat observations of a dining area.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".