Development and Reliability of the Mealtime Social Interaction Measure for Long-Term Care (MSILTC)
Bibliographic record
Abstract
Mealtimes are important social events in retirement (RH) and long term care homes (LTC). This manuscript describes the development, refining and scaling of the MSILTC as well as inter-observer reliability. Two facilities provided access to their RH (n~100) and LTC (n~30-45) dining rooms. This observation-based tool captures both frequency and nature of interactions. Mealtime observations were carried out by trained researchers for development (n=13 tables), refinement (n=12 tables) scaling (n=17 tables) and reliability (n= 30 tables). Tablemate and staff level sub scores are calculated considering number of residents at the table and duration of the meal. Statistical analysis using Cohen's kappa demonstrated that the tool possesses adequate reliability for capturing frequency of interaction among residents and staff [kappa 0.712 and 0.790 respectively]; reliability for nature of interaction was lower [kappa 0.590 and 0.441 respectively]. Construct validity testing is planned to complete the development of the MSILTC.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".