Real-World Evaluation of the Resident Assessment Instrument-Mental Health Assessment System
Bibliographic record
Abstract
OBJECTIVE: We evaluated the Resident Assessment Instrument-Mental Health (RAI-MH) assessment platform at a large psychiatric hospital in Ontario during the 3 years following its provincially mandated implementation in 2005. Our objectives were to document and consider changes over time in front-line coding practices and in indicators of data quality. METHOD: Structured interviews with program staff were used for preliminary information-gathering on front-line coding practices. A retrospective data review of assessments conducted from 2005 to 2007 examined 5 quantitative indicators of data quality. RESULTS: There is evidence of improved data quality over time; however, low scores on the outcome scales highlight potential shortcomings in the assessment system's ability to support outcome monitoring. There was variability in implementation and performance across clinical programs. CONCLUSIONS: This evaluation suggests that the RAI-MH-based assessment platform may be better suited to longer-term services for severely impaired clients than to short-term, highly specialized services. In particular, the suitability of the RAI-MH for hospital-based addictions care should be re-examined. Issues of staff compliance and motivation and problems with assessment system performance would be highly entwined, making it inappropriate to attempt to allocate responsibility for areas of less than optimal performance to one or the other. The ability of the RAI-MH to perform well on clinical front lines is, in any case, essential for it to meet its objectives. Continued evaluation of this assessment platform should be a priority for future research.
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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 | 0.040 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".