Validation of a composite outcome measure for inpatient psychiatry using scales from the interRAI-MH
Bibliographic record
Abstract
Background: Inpatient psychiatry is a critical service in a community-based care system for persons with serious mental illness (SMI). Currently, there are few generally accepted or widely used outcomes to assess the effectiveness of inpatient treatment. Method: Following a Donabedian Model of Health Care Quality, we utilized eight scales from the RAI Mental Health assessment to derive a clinician-scored outcome measure consisting of 4 domains (Psychosis, Depression, Impairment, and Aggression). We combined subscales measuring these domains into a Composite Measure. We used this measure to assess the entire population (N=719) of our large specialized mental health hospital at the beginning (T1) and end (T2) of three months in the hospital (or admission to discharge in shorter stays). We evaluated the content validity of the measure by comparing items and scales with a list of putative contributors to hospital admission (symptoms and complications). To evaluate concurrent validity, we compared mean scores among hospital units with varying lengths of stay and clinical complexity (acute versus chronic versus complex chronic). We used ROC analysis to evaluate the CIIMHS's ability to predict discharge from the hospital. To evaluate construct validity, we examined the measure's responsiveness to changes among patients after treatment in the hospital. Results: We found strong evidence for all four kinds of validity. Conclusions: The composite measure represents a valid measure of inpatient mental health status and will serve as a valuable measure of the quality of care for inpatient psychiatry.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".