The Impact of Enhanced Programming on Aging in Place for People With Dementia in Assisted Living
Bibliographic record
Abstract
BACKGROUND: Assisted living (AL) is a growing and operationally diverse option in our nation's long-term care system. Many consumers view AL communities as a viable option to receive needed services and age in place. However, little is known about the factors that influence residents' ability to age in place when experiencing cognitive decline. OBJECTIVE: To estimate the association of resident and site characteristics to length of stay, reason for leaving and destination for residents with dementia in assisted living. In particular, this study sought to assess the impact of an 'Enhanced' Program intended to facilitate aging in place. METHOD: Data were gathered from a retrospective evaluation of residents' clinical records (N=312) in five dementia-specific ALs (3 with robust enhanced programs) in the Northeastern United States. RESULTS: The time to 50% survival for the full cohort (N=312) was 20.2 months. Both age at move-in and gender were statistically significant predictors of length of stay. Sites with robust support for aging in place exhibited a statistically significant longer length of stay compared to sites with limited support. Of the residents who left or died (N=165) nearly one quarter (24%) were able to stay until the end of their lives, while 52% moved to a nursing home, primarily because of family, financial, or medical concerns. Few residents left these settings because of behavioral problems. CONCLUSION: AL sites with a more robust commitment to an aging in place model and a willingness to provide palliative care demonstrated a significantly longer length of stay.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".