Treatment in a Geriatric Day Hospital improve individualized outcome measures using Goal Attainment Scaling
Bibliographic record
Abstract
BACKGROUND: Evidence regarding outcomes in the Geriatric Day Hospital (GDH) model of care has been largely inconclusive, possibly due to measurement issues. This prospective cohort study aims to determine whether treatment in a GDH could improve individualized outcome measures using goal attainment scaling (GAS) and whether improvements are maintained 6-months post-discharge. METHODS: A total of 469 outpatients admitted to a Canadian Geriatric Day Hospital, between December 2008 and June 2011, were included in the analysis (81.1 ± 6.7 years, 66.3% females); a smaller cohort of 121 patients received a follow-up phone call 6 months following discharge. Baseline, discharge and 6 month post-discharge observer-rated measures of mobility, cognition, and function were completed using GAS. Traditional psychometric measures were also captured. RESULTS: The mean number of goals set was 1.6 (SD 0.8) and patients set goals in the following domains: 88% mobility or falls reduction; 18% optimization of home supports; 17% medication optimization;12% cognition; 8% increasing social engagement; and 5% optimization of function. Total GAS was the most responsive measure to change with 86% of patients improving at discharge; mobility goals were the most likely to be achieved. Six-month GAS scores remained significantly higher than GAS scores on admission. Those who had more goals were more likely to improve during GDH admission (OR 1.49, CI 1.02-2.19) but this association was not seen 6 months after discharge. CONCLUSIONS: This study demonstrated short- and long-term effectiveness of GDH in helping patients achieve individualized outcome measures using GAS.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".