A systematic review of goal attainment scaling implementation practices by caregivers in randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Goal attainment scaling (GAS), an established individualized, patient-centred outcome measure, is used to capture the patient's voice. Although first introduced ~60 years ago, there are few published guidelines for implementing GAS, and almost none for its use when caregivers GAS is implemented with caregiver input. We conducted a systematic review of studies that implemented GAS with caregiver input; and examined variations in GAS implementation, analysis, and reporting. METHODS: Literature was retrieved from Medline, Embase, Cochrane, PsycInfo and CINAHL databases. We included randomized controlled trials (published between 1968 and November 2022) that used GAS as an outcome measure and involved caregiver input during goal setting. RESULTS: Of the 2610 studies imported for screening, 21 met the inclusion criteria. Most studies employed GAS as a primary outcome. The majority (76%) had children as study participants. The most common disorders represented were cerebral palsy, developmental disorders, and dementia/Alzheimer's disease. The traditional five-point GAS scale, with levels from -2 to +2, was most often implemented, with -1 level typically being the baseline. However, most studies omitted essential GAS details from their reports including the number of goals set, number of attainment levels and whether any training was given to GAS facilitators. CONCLUSIONS: GAS with caregiver input has been used in a limited number of randomized controlled trials, primarily in pediatric patients and adults with dementia. There is a variability in GAS implementation and many crucial details related to the specifics of GAS implementation are omitted from reports, which may limit reproducibility. Here we propose catalog that may be utilized when reporting research results pertaining to GAS with caregivers to enhance the application of this patient-centered outcome measure.
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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 | 0.073 | 0.244 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".