Factors Associated With Nursing-Home Entry for Elders in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: As the population ages, a greater demand for long-term care services and, in particular, nursing homes is expected. Policy analysts continue to search for alternative, less costly forms of care for the elderly and have attempted to develop programs to delay or prevent nursing-home entry. Health care administrators require information for planning the future demand for nursing-home services. This study assesses the relative importance of predisposing, enabling, and need characteristics in predicting and understanding nursing-home entry. METHODS: Proportional hazard models, incorporating changes in needs over time, are used to estimate the hazard of nursing-home entry over a 5-year period, using health and sociodemographic characteristics of a representative sample of elderly residents from Manitoba, Canada. RESULTS: After age, need factors have the greatest impact on nursing-home entry. Specific medical conditions have at least as great a contribution as functional limitations. The presence of a spouse significantly reduces the hazard of entry for males only. CONCLUSIONS: The results suggest that the greatest gains in preventing or delaying nursing-home entry can be achieved through intervention programs targeted at specific medical conditions such as Alzheimer's disease, musculoskeletal disorders, and stroke.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".