Measuring Physical and Social Environments in Nursing Homes for People with Middle‐ to Late‐Stage Dementia
Bibliographic record
Abstract
OBJECTIVES: To evaluate measures of dementia care environments by comparing a special care facility (SCF) with traditional institutional facilities (TIFs). DESIGN: A cross-sectional comparative study of nursing home environments conducted as part of a longitudinal study on quality of life for residents with dementia. SETTING: Twenty-four traditional nursing homes and one special care facility. PARTICIPANTS: One SCF with six distinct environments, 24 TIFs with 45 distinct environments, and 88 family members. MEASUREMENTS: Therapeutic Environment Screening Scale-2+ (TESS-2+); Special Care Unit Environmental Quality Scale (SCUEQS), a subset of the TESS-2+ items; Composite Above Average Quality Score (CAAQS), a composite score of all items on the TESS-2+; and Models of Care Instrument (MOCI). RESULTS: The SCUEQS did not detect a significant difference between the SCF and the TIFs (30.0 vs 27.2, P = .28). The CAAQS detected a significant difference between the SCF and the TIFs, whereby the SCF environments were rated as having above-average quality in 71.4% of the domains, compared with 57.3% for the TIF environments (95% confidence interval (CI) for difference = 2.6-25.6%, P = .02). Using the MOCI, SCF families were 1.8 times as likely to rate the SCF as a home or resort versus a hospital as TIF families rating TIFs (95% CI for odds ratio = 1.5-2.1, P < .001). CONCLUSION: The TESS-2+ CAAQS differentiated between physical environments better than the more established SCUQES. The MOCI distinguished between environments using a more holistic approach to measurement. The availability of environmental measures that are able to discriminate between specialized and traditional long-term care settings will facilitate future outcome-based research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".