A multi-site study of the feasibility and clinical utility of Goal Attainment Scaling in geriatric day hospitals
Bibliographic record
Abstract
Background: Goal Attainment Scaling (GAS) is an individualized goal-setting and measurement approach that is useful for patients with multiple, individualized health problems, such as those served by geriatric day hospitals (GDHs) and other specialized geriatric programmes. Purpose: To assess the feasibility and utility of GAS in a multi-site study of six GDH affiliated with the Regional Geriatric Programmes of Ontario. Method: Individualized GAS guides were developed for 15 consecutively admitted patients at each site [total n = 90; mean age: 76.2 SD 8.3; 58.9% female; mean attendances: 24.0 SD 10.3]. Staff members (n = 39) were surveyed on their experience with GAS. Results: Mean goals/patient ranged across sites from 2.1 to 4.3. Mean GAS discharge score was 52.3 SD 8.7, close to the theoretically expected values of 50 SD 10. Common goals included mobility, community reintegration, basic and instrumental activities of daily living, medical issues, cognition/communication, and home safety. Estimated mean time to develop a GAS guide ranged across sites from 15.3 to 43.8 min. Conclusion: Clients were often involved in goal setting; family involvement was less frequent. The staff survey identified challenges and benefits regarding the use of GAS. Study results are being used to inform a more consistent approach to the clinical and research use of GAS in GDH.Implications for RehabilitationThe geriatric day hospital (GDH) has had a long history as one element of a comprehensive system of specialized geriatric services with potential advantages including ongoing treatment and rehabilitation from an interdisciplinary team. Despite this history, the evidence for the effectiveness of GDHs in rehabilitation of older persons has been equivocal.We found that Goal Attainment Scaling (GAS) was able to detect clinically relevant change in this setting which can aid in demonstrating evidence for the utility and impact of GDHs.GAS was feasible in this setting and clinicians felt that GAS may have an effect on speeding up discharge, as a result of having a clearer focus on outcomes that are desired for each patient.Clinicians felt the involvement of clients and families in goals settings resulted in more meaningful outcomes for clients and GAS aided in identifying highly individual outcomes such as quality of life and community integration that are routinely difficult to measure with standardized tools.
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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 | 0.012 | 0.022 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".