Mental health needs of federal female offenders.
Bibliographic record
Abstract
Mental health problems are increasingly being recognized as one of the greatest challenges faced by correctional systems in the effective management of their populations. Over the past decade, the number of federally sentenced female offenders in Canada presenting with mental health problems has risen significantly, from 13% in 1996/1997 to 29% in 2008/2009 (Correctional Service of Canada, 2009a). This research used the screener version of the Computerized Diagnostic Interview Schedule (C-DIS-IV; n = 88) to outline the mental health needs of federally sentenced females in Canada. Results provide evidence for extremely elevated rates for certain diagnoses and confirm substance dependence as a significant area of need in this sample. Moreover, alcohol dependence emerged as an area of particular concern for Aboriginal women. Furthermore, all individuals experiencing a lifetime substance dependence disorder have also suffered from an additional psychiatric diagnosis at some point in their lives; thereby highlighting the possible levels of concurrent disorders among this population. This research highlights the critical importance of comprehensive and ongoing mental health assessment, and treatment, for the successful management and reintegration of female offenders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".