Setting individualised goals for people living with dementia and their family carers: A systematic review of goal-setting outcome measures and their psychometric properties
Bibliographic record
Abstract
BackgroundIndividualised goal-setting outcome measures can be a useful way of reflecting people living with dementia and family carers’ differing priorities regarding quality-of-life domains in the highly heterogeneous symptomatology of the disease. Evaluating goal-setting measures is challenging, and there is limited evidence for their psychometric properties.Aim(1) To describe what goal-setting outcomes have been used in this population; (2) To evaluate their validity, reliability, and feasibility in RCTs.MethodWe systematically reviewed studies that utilised goal-setting outcome measures for people living dementia or their family carers. We adapted a risk of bias and quality rating system based on the COSMIN guidelines to evaluate the measurement properties of outcomes when used within RCTs.ResultsThirty studies meeting inclusion criteria used four different goal-setting outcome measures: Goal Attainment Scaling (GAS), Bangor Goal Setting Interview (BGSI), Canadian Occupational Performance Measure (COPM) and Individually Prioritized Problems Assessment (IPPA); other papers have reported study-specific goal-setting attainment systems. Only GAS has been used as an outcome over periods greater than 9 months (up to a year). Within RCTs there was moderate quality evidence for sufficient content validity and construct validity for GAS, COPM and the BGSI. Reliability was only assessed in one RCT (using BGSI); in which two raters reviewed interview transcripts to rate goals with excellent inter-rater reliability. Feasibility was reported as good across the measures with a low level of missing data.ConclusionWe found moderate quality evidence for good content and construct validity and feasibility of GAS, BGSI and COPM. While more evidence of reliability of these measures is needed, we recommend that future trials consider using individualised goal setting measures, to report the effect of interventions on outcomes that are most meaningful to people living with dementia and their families.
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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 | 0.030 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".