Adaptation of the Clinical Global Impression for Use in Correctional Settings: The CGI-C
Bibliographic record
Abstract
Background Provision of mental health care in correctional settings presents unique challenges. There is a need for a simple to use tool to measure severity of mental illness in correctional settings that can be used by mental health staff from different disciplines. We adapted the severity scale of the Clinical Global Impression for use in correctional settings which we have called CGI-C, and carried out a reliability study. Method Clinical descriptions of typical inmate presentations were developed to benchmark each of the seven possible ratings of the CGI. Twenty-one case vignettes were then developed for study of inter-rater reliability, which were then rated using the CGI-C by 5 forensic psychiatrists (on three occasions), and 11 multidisciplinary healthcare clinicians (twice). The tool was introduced into clinical practice and the first 57 joint assessments carried out by both a psychiatrist and clinician in which a CGI-C was rated were compared to measure inter-rate reliability. Results We found very good inter-rater and test-retest reliability in all analyses. Gwet’s AC, calculated on initial ratings of the vignettes by the psychiatrists was 0.85, (95% CI 0.81-0.90, p<0.001), and 0.87, (95% CI 0.83-0.91, p<0.001) for clinician ratings. Inter-rater reliability based on 57 joint face-to-face assessments of inmates showed Gwet’s AC coefficient of 0.93 (95 % CI 0.88-0.97). Conclusion The CGI-C is simple to use, can be used by members of the multidisciplinary team and shows high reliability. The advantage in correctional settings is that it can be used even with the most severely ill and behaviorally disturbed, based on observation and collateral information.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".