Results of the Computerized Mental Health Screening System for Female Offenders
Bibliographic record
Abstract
Objective: To estimate the proportion of incoming female offenders in the Canadian federal correctional system requiring mental health services based on results of a Computerized Mental Health Intake Screening System (CoMHISS). Method: Consecutive admissions to the five regional women’s prisons in the Correctional Service of Canada were approached to consent to participate in the CoMHISS. The screening process combines two psychological self-report measures, the Brief Symptom Inventory, and the Depression Hopelessness and Suicide Screening Form. Results were analyzed based on the percentage of women who met established cut-off scores on the measures and further analyzed by Aboriginal ethnicity. Results: Sixty-two percent of the sample reported elevated levels of psychological distress that would warrant further assessment. Although higher for Aboriginal women, the mean scores did not differ significantly from those of non-Aboriginal women. The rate of co-occurring substance abuse among women reporting psychological distress was estimated at 70%. Conclusions: Planning for the delivery of mental health services for federally sentenced women should consider their high rates and variety of psychological problems. Specific correctional treatment planning requires attention to criminogenic needs as well as mental health issues and serious substance abuse problems. Mental health providers for women in prison should be aware of the significant likelihood of co-morbid substance abuse disorders and mental health problems and prepare women to identify follow up services to address these problems on release.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".