Routine use of mental health outcome assessments: choosing the measure
Bibliographic record
Abstract
BACKGROUND: There is little consensus about which outcome measures to use in mental healthcare. AIMS: To investigate the relationship between the items in four staff-rated measures recommended for routine use. METHOD: Correlation analysis of total scores and factor analysis using combined data from the Health of the Nation Outcome Scales (HoNOS). The Camberwell Assessment of Need Short Appraisal Schedule (CANSAS), the Threshold Assessment Grid (TAG) and the Global Assessment of Functioning (GAF) were performed. Procrustes analysis on factors and scales, and Ward's cluster analysis to group the items, were applied. RESULTS: The total scores of the measures were moderately correlated. The Procrustes analysis, factor analysis and cluster analysis all agreed on better coverage of the patients' problems by HoNOS and CANSAS. CONCLUSIONS: A global severity factor accounts for 16% of the variance, and is best measured with TAG or GAF. The CANSAS and HoNOS each provide a detailed characterisation of the patient; only CANSAS provides information about met needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".