Validation of the brief version of the Recovery Self-Assessment (RSA-B) using Rasch measurement theory.
Bibliographic record
Abstract
OBJECTIVE: In psychiatry, the recovery paradigm is increasingly identified as the overarching framework for service provision. Currently, the Recovery Self-Assessment (RSA), a 36-item rating scale, is commonly used to assess the uptake of a recovery orientation in clinical services. However, the consumer version of the RSA has been found challenging to complete because of length and the reading level required. In response to this feedback, a brief 12-item version of the RSA was developed (RSA-B). This article describes the development of the modified instrument and the application of traditional psychometric analysis and Rasch Measurement Theory to test the psychometrics properties of the RSA-B. METHODS: Data from a multisite study of adults with serious mental illnesses (n = 1256) who were followed by assertive community treatment teams were examined for reliability, clinical meaning, targeting, response categories, model fit, reliability, dependency, and raw interval-level measurement. Analyses were performed using the Rasch Unidimensional Measurement Model (RUMM 2030). RESULTS: Adequate fit to the Rasch model was observed (χ2 = 112.46, df = 90, p = .06) and internal consistency was good (r = .86). However, Rasch analysis revealed limitations of the 12-item version, with items covering only 39% of the targeted theoretical continuum, 2 misfitting items, and strong evidence for the 5 option response categories not working as intended. CONCLUSIONS: This study revealed areas for improvement in the shortened version of the 12-item RSA-B. A revisit of the conceptual model and original 36-item rating scale is encouraged to select items that will help practitioners and researchers measure the full range of recovery orientation.
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".