Long-Term Care in the St. John's Region: Impact of Single Entry and Prediction of Bed Need
Bibliographic record
Abstract
In 1996, the St. John's region had a population of 8,435 > or = 75 years, with 996 nursing home (NH) beds and 550 supervised care (SC) beds. A single entry system to these institutions was implemented in 1995. To determine the impact of the single entry system, the demographic and clinical characteristics of NH residents were assessed in 1997 (N = 1,044) and in 2003 (N = 963). To determine the efficiency of placement and the need for long-term care beds, two incident cohorts requesting placement were studied in 1995/96 (N = 467) and in 1999/2000 (N = 464). Degree of disability was determined using the Residents Utilization Groups III classification (RUG-III) and the Alberta Resident Classification Score (ARCS), and time to placement and to death was measured. In prevalent NH residents, the percentage without RUGS-III disability decreased from 18.5% in 1997 and to 9.9% in 2003. The proportion recommended for NH was 75% in 1995/96 and 72% in 1999/2000, despite the fact that the proportion with RUGS-III disability was 64% in both periods. Using a decision tree, optimal placement for the 1999/2000 cohort was 36% to SC, 20% to SC for the cognitively impaired, and 44% to NH. Predicted need for long-term care beds in 2004 matched poorly with current provision of NH and SC beds, and the mismatch will be worse in 2014. It was concluded that the single entry system was associated with improved appropriateness of NH bed utilization. However, there was a mismatch in need for and provision of institutional long-term care. Investment in the reconfiguration of long-term care beds by case mix and by geography is necessary.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".