National Prevalence of Mental Disorders among Incoming Canadian Male Offenders
Bibliographic record
Abstract
OBJECTIVE: A current estimate of prevalence rates of mental disorder among Canadian federal offenders is required to facilitate treatment delivery and service planning. METHOD: The study determined prevalence rates of major mental disorders among newly admitted male offenders entering the federal correctional system in Canada. Data were collected at each regional reception site on consecutive admissions for a 6-month period (N = 1110). Lifetime and current prevalence rates were estimated using the Structured Clinical Interview for DSM Axis I Disorders (SCID-I) and the SCID Axis II Disorders (SCID-II). Degree of impairment was estimated using the Global Assessment of Functioning (GAF) scale. Results were disaggregated by Aboriginal ancestry. RESULTS: The national prevalence rate for any current mental disorder was 73%. The highest rates were for alcohol and substance use disorders; however, over half of participants met the lifetime criteria for a major mental disorder other than alcohol or substance use disorders or antisocial personality disorder. Thirty-eight percent met the criteria for both a current mental disorder and one of the substance use disorders. Fifty-seven percent of offenders with a current Axis I mental disorder were rated as experiencing minimal to moderate functional impairment based on the GAF, indicating that most participants do not require intensive psychiatric services. CONCLUSIONS: These results underscore the challenge posed to Canadian federal corrections in providing the necessary mental health services to assist in the management and rehabilitation of a significant percentage of the offender population with mental health needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".