Linking Environments and Resident Function via MDS
Bibliographic record
Abstract
Abstract Over the past three decades, research in long-term care settings has demonstrated links between design characteristics and the experiences of residents who live in these environments. Individual studies have shown how different approaches to the design of the built environment can contribute to enhanced resident safety, function, and even care outcomes but these findings have been difficult to generalize due to the lack of a validated and comprehensive assessment tool that provides a consistent metric for the discrete environmental characteristics that may be contributing factors. In addition, existing environmental tools have limited demonstrated connections to other widely used assessment tools for care outcomes such as the Minimum Data Set (MDS) the standard for facilitating care management in nursing homes. The Environmental Audit Scoring Evaluation (EASE) tool has been shown to provide a reliable set of comprehensive scoring criteria that can measure the strengths (or weaknesses) of specific design features. Further, the EASE tool items are comprised of features that specifically support quality outcomes measures that are also of focus of the MDS. In this paper, we describe the study design to link the MDS assessments of 235 residents to the EASE assessments of their (15) distinct living areas across seven skilled care buildings.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".