Utility of goal setting outcome measures for people living withdementia and their family carers
Bibliographic record
Abstract
Background: Traditional outcome measures in dementia research often fail to reflect the priorities of people living with dementia and their family carers. Goal Attainment Scaling (GAS) is a personalised, patient generated outcome measure that enables participants to define and track progress towards individualised goals, but its psychometric properties, feasibility, and implementation in dementia trials remain under-explored. Aim: To evaluate the feasibility, validity, reliability, and implementation of GAS as a personalised outcome measure in psychosocial dementia research. Methods: I systematically reviewed studies evaluating the psychometric properties of goal-setting outcome measures in randomised controlled trials (RCTs) of dementia interventions. I then evaluated GAS within the NIDUS-Family (New Interventions in Dementia Study) RCT including: (1) a content analysis of 1043 baseline goals set by 302 participant dyads; (2) the feasibility, fidelity, and psychometric properties of GAS when delivered by non-clinical facilitators and scored at 6- and 12-month follow-ups. Findings: My review identified three commonly used measures: GAS, the Bangor Goal-Setting Interview (BGSI), and the Canadian Occupational Performance Measure (COPM). GAS showed strong content validity but not reliability. In NIDUS-Family, GAS was feasible to deliver remotely by non-clinical facilitators with training and supervision. Goals were person-centred, diverse, and aligned with intervention targets. GAS showed excellent inter-rater reliability (ICC = 0.99) and strong intra-rater reliability (κ = 0.91). Weak to modest correlations with quality-of-life measures support construct validity and suggest GAS captures additional constructs relevant to lived experience. Predictive validity was shown by higher GAS scores predicting the person with dementia remaining at home. Conclusion: For the first time, I show GAS is a feasible, reliable, and valid personalised outcome measure that can be delivered by non-clinical facilitators in a psychosocial dementia RCT. Despite methodological challenges, GAS aligns with person-centred care and offers a scalable, meaningful alternative to standardised outcome measures in dementia research.
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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 | 0.087 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".