Bibliographic record
Abstract
The St. John's region in Newfoundland, Canada had a population of 8435 ≥ 75 years in 1996, with 996 nursing home (NH) beds and 550 supervised care (SC) beds. However, only 116 SC beds were available at this time in the city of St. John's, where the majority of this at risk population lived. A single entry system to these institutions was implemented in 1995. To determine the need for long term care (LTC) two incident cohorts requesting placement were studied in 1995/96 (n=467) and in 1999/00 (n=464). Degree of disability was determined using the Residents Utilization Groups-III Classification (RUGs) and the Alberta Resource Classification System (ARCS). Time to placement and survival were measured. Factors predicting placement into LTC and mortality were determined. To determine the impact of the single entry system, clients of six NHs were assessed in 1997 (n=1044) and in 2003 (n=963). -- The number requiring placement increased from 392 to 431 from 1995/96 to 1999/00, an increase of 10% over four years. The population increase in those ≥ 75 years during this time was 8%. Comparing the two time periods, demographic characteristics were similar in the two incident cohorts. The proportion with no indicators for NH was the same (36%), and the proportion sent to SC was 25 and 28% in 1995/96 and 1999/00, respectively. There was no difference in RUGs classification between the two incident cohorts and the proportion classified as high level of care i.e., 6/7 on ARCS remained the same (22 vs. 23%). NH clients in 2003 differed from those in 1997; in 2003 the mean length of stay was shorter (3.7 vs. 4.5 years); the proportion with no indicators for NH care was smaller (10 vs. 19%); the proportion requiring special care/clinically complex was higher (45 vs. 30%); and the proportion with a low level ARCS i.e., 1/2 was smaller (16 vs. 25%). This suggests that clients admitted to NH care following the start of a single entry system were more appropriately placed than before. Time to placement was unchanged for SC and NH care comparing both time periods. Time to placement in SC was much faster than in NHs. Independent factors which influenced time to placement included residence, RUGs, panel recommendation, sex, and age. Time from panel assessment to death for those recommended for SC was unchanged in both incident cohorts (3.09 vs. 3.02 years), as was those recommended for NH (2.35 vs. 2.23 years). Independent factors that influenced mortality included RUGs, sex and age. Using optimal methods of placement in 1995/96, as defined by a decision tree, the need for NHs decreased (75 to 37%); for SC increased (25 to 37%); and SC for cognitive impairment (CI) was 26%. In 1999/00, the need for NHs decreased (72 to 44%); for SC increased (28 to 36%); and SC for CI was 20%. Using optimal methods of placement, a deficit of 253 SC beds in the city and an excess of 235 outside the city would occur by 2014. An excess of 692 NH beds in the city and a deficit of 164 outside the city will exist. A total of 251 SC beds for the CI are crucial. -- It was concluded that the St. John's region had an excess of NH beds and a geographic imbalance of SC beds leading to over-utilization of NH beds. The single entry system succeeded in improving the appropriateness of utilization of NH beds. Nonetheless, SC facilities for the elderly with modest disability and for those with CI are necessary, as is a reduction in NH beds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".