The Environmental Audit Screening Evaluation: Establishing Reliability and Validity of an Evidence-Based Design Tool
Bibliographic record
Abstract
Background and Objectives: Current assessment tools for long-term care environments have limited generalizability or ability to be linked to specific quality outcomes. To discriminate between different care models, tools are needed to assess important elements of the environmental design. The goal of this project was to systematically evaluate the reliability and validity of the Environmental Audit Screening Evaluation (EASE) tool to better enable the identification of best models in long-term care design to maintain quality of life for persons with dementia and their caregivers. Research Design and Methods: Twenty-eight living areas (LAs) were selected from 13 sites similar in organizational/operational commitment to person-centered care but with very different LA designs. LAs were stratified into 3 categories (traditional, hybrid, and household) based primarily on architectural/interior features. Three evaluators rated each LA using the Therapeutic Environment Screening Scale (TESS-NH), Professional Environmental Assessment Protocol (PEAP), Environmental Audit Tool (EAT-HC), and EASE. One of each type of LA was reassessed approximately 1 month after the original assessment. Results: = 0.82 and 0.71, respectively). Analysis of variance indicated that the EASE distinguished between traditional and home-like settings (0.016), but not hybrid LAs. Interrater and inter-occasion reliability and agreement of the EASE were consistently high. Discussion and Implications: Neither of the 2 U.S.-based existing environmental assessment tools (PEAP and TESS-NH) discriminated between the 3 models of environments. The EAT-HC was most closely aligned with the EASE and performed similarly in differentiating between the traditional and household models, but the dichotomous scoring of the EAT-HC fails to capture environmental nuances. The EASE tool is comprehensive and accounts for nuanced design differences across settings.
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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 | 0.216 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".