Examining the Structure and Utility of the EASE: Environmental Audit Scoring Evaluation Tool
Bibliographic record
Abstract
Abstract The designed environment is especially important for individuals with Alzheimer’s Disease and related dementias, but validated tools to assess physical environments lag behind theory and practice and omit best design practices. The Environmental Audit Scoring Evaluation tool (EASE) is a unique assessment tool for long-term care settings that is: 1) evidence-based, 2) dementia-inclusive (for both segregated and integrated living areas), and 3) specifically inclusive of household model settings. Founded on the principles of person-centered care practices, the tool specifically targets environmental characteristics that distinguish the values of residential living over institutional routines. The tool was administered in 228 living areas across US and Canada, including nursing homes, assisted living and memory care. The first paper addresses the analysis of the factorial structure of the EASE tool. The second paper examines environmental characteristics that differentiate between traditional versus person-centered care settings. The third paper explores the relationship between EASE evaluations and resident function as assessed with the MDS, identifying associations between environmental characteristics and outcomes of interest. The final paper explicates the use of the EASE as a tool to inform the process of change in seven traditional care settings seeking to become more person-centered.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".