The relationship between self-reported substance use and psychiatric symptoms in low-threshold methadone maintenance treatment clients
Bibliographic record
Abstract
BACKGROUND: Ongoing psychiatric symptoms and substance use are common difficulties experienced by clients enrolled in methadone maintenance treatment (MMT). However, little research to date has evaluated if specific types of current substance use are related to specific types of current psychiatric symptoms. The present study investigated these relationships with a sample of clients enrolled in a low-threshold MMT program (i.e., clients are not expelled if they continue to use substances). Some clients enrolled in low-threshold programs may never achieve complete abstinence from all substances. Thus, understanding the possibly perpetuating relationships between concurrent substance use and psychiatric symptoms is important. Understanding such relationships may aid in developing possible target areas of treatment to reduce substance use and/or related harms in this population. METHODS: Seventy-seven individuals were interviewed regarding methadone usage and current and past substance use. Current psychiatric symptoms were assessed using a modified version of the Psychiatric Diagnostic Screening Questionnaire (PDSQ). Relationships between types of substances used in the past 30 days and the types and number of psychiatric symptoms experienced in the same timeframe were examined. RESULTS: The majority of participants (87.0%) reported using alcohol, illicit substances, non-prescribed prescription opioids, or non-prescribed benzodiazepines in the past 30 days and 77.9% of participants reported currently experiencing psychiatric symptoms at levels that would likely warrant diagnosis. Current non-prescribed benzodiazepine use was a predictor for increased severity (i.e., symptom count) of almost all anxiety and mood disorders assessed. Conversely, number and presence of generalized anxiety symptoms and presence of social phobia symptoms predicted current non-prescribed benzodiazepine and alcohol use, respectively. CONCLUSIONS: Individuals enrolled in the present low-threshold MMT program experience a wide variety of psychiatric symptoms and continue to use a variety of substances, including opioids. There was a particularly consistent pattern of associations between non-prescribed benzodiazepine use and a variety of psychiatric symptoms (particularly anxiety) suggesting that addressing concurrent illicit benzodiazepine use and anxiety symptoms in MMT clients warrants further clinical attention and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".