Co-Occurrence of Substance use Disorders with other Psychiatric Disorders: Implications for Treatment Services
Bibliographic record
Abstract
Introduction This paper critically evaluates the literature on the co-occurrence of substance-use disorders (SUDs) with other psychiatric conditions. Our review considers the variety of different associations between the two, and suggests the implications of the literature for the design of treatment services that address both types of disorders. Methods: A narrative review of research and theory was conducted, covering epidemiology of co-occurring psychiatric disorders worldwide, mechanisms underlying co-occurrence, and treatment models. Results: Epidemiological research has documented a high prevalence of co-occurring disorders in both clinical samples and the general population, although the literature is based primarily on studies in high-income countries and some of the overlap might be due to the co-occurrence of milder forms of both types of disorders. Consistent with what has been reported in other reviews, we conclude that clients with co-occurring disorders tend to have a more severe course of illness, more severe health and social consequences, more difficulties in treatment, and worse treatment outcomes than clients with a single disorder; we address the implications of these findings for the design of treatment services. Conclusions: Much of the evidence shows that separately, treatments for both SUD and other psychiatric disorders are effective in reducing substance use and in improving behavioral, familial, and psychosocial outcomes. The evidence further suggests that these outcomes might be improved when treatment modalities are offered in combination within an integrated treatment plan that simultaneously addresses substance abuse and psychiatric problems. It is concluded that there is potentially more to be gained from taking a public health perspective and working on efforts to implement existing evidence-based practices at the systems level, than from the current tendency to look for ever more powerful individual-level interventions at the clinical level.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".