High rates of hospital admission among older residents in assisted living facilities: opportunities for intervention and impact on acute care.
Bibliographic record
Abstract
BACKGROUND: Little is known about health or service use outcomes for residents of Canadian assisted living facilities. Our objectives were to estimate the incidence of admission to hospital over 1 year for residents of designated (i.e., publicly funded) assisted living (DAL) facilities in Alberta, to compare this rate with the rate among residents of long-term care facilities, and to identify individual and facility predictors of hospital admission for DAL residents. METHODS: Participants were 1066 DAL residents (mean age ± standard deviation 84.9 ± 7.3 years) and 976 longterm care residents (85.4 ± 7.6 years) from the Alberta Continuing Care Epidemiological Studies (ACCES). Research nurses completed a standardized comprehensive assessment for each resident and interviewed family caregivers at baseline (2006 to 2008) and 1 year later. We used standardized interviews with administrators to generate facility- level data. We determined hospital admissions through linkage with the Alberta Inpatient Discharge Abstract Database. We used multivariable Cox proportional hazards models to identify predictors of hospital admission. RESULTS: The cumulative annual incidence of hospital admission was 38.9% (95% confidence interval [CI] 35.9%- 41.9%) for DAL residents and 13.7% (95% CI 11.5%-15.8%) for long-term care residents. The risk of hospital admission was significantly greater for DAL residents with greater health instability, fatigue, medication use (11 or more medications), and 2 or more hospital admissions in the preceding year. The risk of hospital admission was also significantly higher for residents from DAL facilities with a smaller number of spaces, no licensed practical and/ or registered nurses on site (or on site less than 24 hours a day, 7 days a week), no chain affiliation, and from select health regions. INTERPRETATION: The incidence of hospital admission was about 3 times higher among DAL residents than among long-term care residents, and the risk of hospital admission was associated with a number of potentially modifiable factors. These findings raise questions about the complement of services and staffing required within assisted living facilities and the potential impact on acute care of the shift from long-term care to assisted living for the facility-based care of vulnerable older people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".