A Review of Instruments for Measuring Functional Recovery in Those Diagnosed With Psychosis
Bibliographic record
Abstract
The task of judging an individual's functional recovery is not an easy one for healthcare professionals. Indeed, increasing one's accuracy in predicting one's ability to self-maintain would be of great value for determining if functional recovery has or is occurring. The purpose of this review is to examine existing measures for assessing remission/normalization of functional status among people with psychosis. Our review evaluates 8 measures of functional ability encompassing self-report, clinical, and performance-based measures. We elected to utilize a grading system to aid readers in understanding the merit of a scale for use in assessing functional recovery. In this approach, a letter grade (A, B, or C) was assigned to each of 4 domains we deemed important to professionals in electing to use specific assessments: (1) Ease of Administration, (2) Reliability, (3) Validity/Relationship to Real-World Outcomes, and (4) Sensitivity to Change/Use in Clinical Trials. Results indicated that no "gold standard" measure has been developed to date, but performance-based measures appear to have the most evidence for predicting concurrent self-maintenance abilities (eg, residing independently or maintaining work). More research on existing measures is needed, and greater funding for developing new measures of functional recovery is strongly recommended.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".