Perceptions of family and staff on the role of the environment in long-term care homes for people with dementia
Bibliographic record
Abstract
BACKGROUND: Disruptive behaviors are frequent and often the first predictor of institutionalization. The goal of this multi-center study was to explore the perceptions of family and staff members on the potential contribution of environmental factors that influence disruptive behaviors and quality of life of residents with dementia living in long-term care homes. METHODS: Data were collected using 15 nominal focus groups with 45 family and 59 staff members from eight care units. Groups discussed and created lists of factors that could either reduce disruptive behaviors and facilitate quality of life or encourage disruptive behaviors and impede the quality of life of residents. Then each participant individually selected the nine most important facilitators and obstacles. Themes were identified from the lists of data and operational categories and definitions were developed for independent coding by four researchers. RESULTS: Participants from both family and staff nominal focus groups highlighted facility, staffing, and resident factors to consider when creating optimal environments. Human environments were perceived to be more important than physical environments and flexibility was judged to be essential. Noise was identified as one of the most important factors influencing behavior and quality of life of residents. CONCLUSION: Specialized physical design features can be useful for maintaining quality of life and reducing disruptive behaviors, but they are not sufficient. Although they can ease some of the anxieties and set the stage for social interactions, individuals who make up the human environment are just as important in promoting well-being among residents.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".