Predictors of Alcohol and Drug Dependence
Bibliographic record
Abstract
OBJECTIVE: Our study sought to identify sociodemographic, clinical, life perception, and service use characteristics that distinguish new cases of people dependent on substances from the general population; and to determine predictors of substance dependence over a 2-year period. Variables that differentiate people dependent on substances according to sex and age were also assessed. METHODS: Among 2434 people who took part in an epidemiologic catchment area health survey at baseline, 2.2% were identified with substance dependence at the second measurement time only. Using a comprehensive framework, various aspects were considered as predictors for multivariate statistics. RESULTS: Participants with substance dependence at time 2 only showed worse clinical conditions, life events, life and health perception, and neighbourhood characteristics than other participants, but only 2.5% used health care services. Male sex, younger age, stigmatization, and impulsiveness were predictors of substance dependence. Regarding sex, females with dependence were only more likely to suffer from social phobia than males. In terms of age categories, participants over 50 with substance dependence were more likely to have a lower household income and less social support than younger people. CONCLUSION: Stigmatization was the strongest predictor of substance dependence. Our study also confirmed that males and younger people were more likely to have substance dependence. Anti-stigmatization, prevention, and outreach programs are needed to overcome the reluctance of this clientele to use health care services. Health professionals should also pay more attention to life and health perception and neighbourhood characteristics of newly identified drug users.
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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".