Medical and Mental Health Status among Drug Dependent Patients Participating in a Smoking Cessation Treatment Study
Bibliographic record
Abstract
Substance Abusers have a large number of medical and psychiatric problems, and 70-90% are smokers. The aim of this analysis was to examine the prevalence and correlates of medical and psychiatric problems in this sample of drug dependent patients who were participants in a multi-site study of smoking cessation interventions while engaged in substance abuse treatment. Descriptive analyses showed at baseline, 72.8% of participants had at least one medical problem and 64.1% had at least one psychiatric diagnosis. Medical problems correlated strongly with age, smoking severity, and pack-years; Psychiatric problems correlated with gender and ethnicity. Smoking cessation treatment was associated with a moderate reduction in the ASI Medical composite score. More research is needed on the possible effects of combined treatment of substance abuse and concurrent medical and psychiatric problems. Offering smoking cessation in conjunction with primary care may be a way to address the health needs of this population.
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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 | 0.001 | 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".