Prevalence and Profile of People with Co-Occurring Mental and Substance Use Disorders within a Comprehensive Mental Health System
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence and profile of people with co-occurring mental and substance use disorders in relation to numerous demographic, diagnostic, and needs-related variables across a comprehensive system of mental health services using a standard methodology. METHOD: Data were collected on cases (n = 9839) sampled from specialty tertiary inpatient, specialty outpatient, and community-based mental health programs. Status with respect to co-occurring disorders was based on recorded diagnosis of substance use disorder and the substance abuse measure within the Colorado Client Assessment Record. The demographic and needs profile was compared across groups with or without co-occurring disorders within each level of care. RESULTS: Overall, the prevalence of co-occurring disorders was 18.5%, and highest among clients receiving specialty tertiary inpatient care (28%), and within selected subpopulations such as younger adults (55%) and those with personality disorders (34%). There were few differences between groups based on co-occurring disorders in the specialty inpatient programs. For outpatient and community settings, the clients with co-occurring disorders were distinguished by a more impaired and complex needs profile and more likely to be young, single, male, and of low education. Across all levels of care, having a co-occurring disorder was strongly associated with antisocial and challenging behaviour, legal involvement, and risk of suicide or self-harm. CONCLUSION: The prevalence estimate of co-occurring disorders is likely representative of a multilevel system of care that serves a large, mixed urban and rural population. Results highlight the need to focus on specific subpopulations and sectors in pursuit of more integrated treatment and support for their mental health and addictions problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".