Using a Structured Environmental Assessment Tool of Inform Change Under Constraints: Action Marguerite
Bibliographic record
Abstract
Abstract One goal of the EASE is to assist care providers who have older, traditional buildings determine how to best utilize scarce resources when considering renovations that will have the most positive impact on residents, families and staff. Analysis of differences between settings that clearly reflect traditional design elements (long, double loaded corridors and minimal space for meaningful social engagement) and settings that have adopted some person-centered care values identify smaller scale and the presence of a functional kitchen as key factors. Both of these involve significant operational as well as environmental changes and can be met with resistance. This session describes the process of working with a care provider that had seven highly traditional living areas assessed with the EASE which resulted in identification of specific recommendations to reduce the size of living areas and include functional kitchens. The evidence-based nature of the EASE supports constructive responses to perceived barriers to adoption of changes that require operational changes. The EASE also identifies numerous low-cost interventions that can be easily adopted allowing for a phased implementation process.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".