Prevalence of Mental Health Needs, Substance Use, and Co-occurring Disorders Among People Admitted to Prison
Bibliographic record
Abstract
OBJECTIVE: People who are incarcerated experience social exclusion and have higher rates of mental and substance use disorders than the general population. Prisons are not suitable for treating mental illness, and understanding how the profile of prison populations changes provides essential information for correctional service planning. This study examined changes in the prevalence of mental and substance use disorders among people admitted to provincial prisons in British Columbia (BC), Canada. METHODS: The study included all people admitted to any of the 10 provincial prisons in BC from 2009 through 2017 (N=47,117). Using the Jail Screening Assessment Tool, a validated intake screening tool designed for rapid identification of mental health needs, the authors calculated the period prevalence (by calendar year) of mental health needs, substance use disorders, and drug use. RESULTS: The proportion of people with co-occurring mental health needs and substance use disorders increased markedly per year, from 15% in 2009 to 32% in 2017. Prevalence of methamphetamine use disorder increased nearly fivefold, from 6% to 29%, and heroin use disorder increased from 11% to 26%. The proportion of people with any mental health need and/or substance use disorder increased from 61% to 75%. CONCLUSION: The clinical profile of people admitted to BC prisons has changed, with dramatic increases in the proportion of people with co-occurring disorders and reported methamphetamine use. More treatment and efforts to address social and structural inequities for people with complex clinical profiles are required in the community to reduce incarceration among people with multifaceted and complex mental health care needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".