The responsiveness of goal attainment scaling using just one goal in controlled clinical trials: an exploratory analysis
Bibliographic record
Abstract
BACKGROUND: Goal Attainment Scaling (GAS) is an individualized outcome measure that allows the setting of personalized treatment goals. We compared the responsiveness of GAS when individuals set only one goal instead of the recommended three or more goals. METHODS: We conducted exploratory analyses on data from two randomized controlled trials: the Video-Imaging Synthesis of Treating Alzheimer's Disease (VISTA) (n = 130); and the Mobile Geriatric Assessment Team (MGAT) (n = 265). Independent t-tests and standardized response means (SRMs) were used to assess responsiveness of one- vs. multiple-goal GAS. RESULTS: In VISTA, clinician-rated multiple-goal GAS detected higher goal attainment in the intervention group (p = 0.01; SRM = 0.48). One-goal GAS, whether rated by patients or by clinicians, did not detect differences in goal attainment between groups (patient: p = 0.56, SRM = 0.10; clinician: p = 0.10, SRM = 0.29). In MGAT, multiple-goal GAS (outcome goals: p < .001, SRM = 1.29; total goals: p < .001, SRM = 1.52) and one-goal GAS (outcome goals: p < .001, SRM = 0.89; total goals: p < .001, SRM = 0.75), detected significantly higher goal attainment in the intervention group. CONCLUSION: One-goal GAS detected significant change in response to a patient-centred, multi-domain care initiative. As such, in similar contexts, one-goal GAS may be an effective means of optimizing personalization and improving GAS feasibility through reduced administration time. However, it is not yet clear if one-goal GAS is responsive in the context of a pharmacological intervention and further research is recommended.
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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 | 0.467 | 0.587 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".