Development of a Physical Environmental Observational Tool for Dining Environments in Long-Term Care Settings
Bibliographic record
Abstract
Purpose: This paper presents the first standardized physical environmental assessment tool titled Dining Environment Audit Protocol (DEAP) specifically designed for dining spaces in care homes and reports the results of its psychometric properties. Items rated include: adequacy of lighting, glare, personal control, clutter, staff supervision support, restraint use, and seating arrangement option for social interaction. Two scales summarize the prior items and rate the overall homelikeness and functionality of the space. Methods: Ten dining rooms in three long-term care homes were selected for assessment. Data were collected over 11 days across 5 weeks. Two trained assessors completed DEAP independently on the same day. Interrater-reliability was completed for lighting, glare, space, homelike aspects, seating arrangements and the two summary scales, homelikeness and functionality of the space. For categorical measures, measure responses were dichotomized at logical points and Cohen's Kappa and concordance on ratings were determined. Results: The two overall rating scales on homelikeness and functionality of space were found to be reliable intraclass correlation coefficient (ICC) (~0.7). The mean rating for homelikeness for Assessor 1 was 3.5 (SD 1.35) and for functionality of the room was 5.3. (SD 0.82; median 5.5). Implications: The findings indicate that the tool's interrater-reliability scores are promising. The high concordance on the overall scores for homelikeness and functionality is indicative of the strength of the individual items in generating a reliable global assessment score on these two important aspects of the dining space.
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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 | 0.045 |
| 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 | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".