A Systematic Review of Validation Practices for the Goal Attainment Scaling Measure
Bibliographic record
Abstract
Goal attainment scaling (GAS) is an internationally recognized measure that is widely used in educational, counseling, and clinical settings to identify and evaluate relevant goals for an individual. The GAS is an unusual measure because its content, which consists of goals, is formed by the respondent and/or users in the process of completing the GAS. Using the unified view of validity as a guiding framework, this systematic review examines validation practices and how goals are represented in this measure. This review demonstrates that validation practices tend to focus on aspects that do not support the overall construct validity of the measure, as well as reference to the GAS measure or GAS scores as a property. Several gaps in validity evidence and the various ways goals are conceptualized are described and discussed. The varying ways goals are considered suggest clarity is needed to enhance explanations and score meaning. This review urges researchers to consider ways validity and validation evidence can help verify the many claims that are made about this measure. Future validity research needs to consider application of a theoretical framework and response processes as key aspects of substantiating the construct measured by the GAS.
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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 | 0.044 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".