Correlates of methamphetamine use among young Iranians: Findings of a population‐based survey in 2013
Bibliographic record
Abstract
INTRODUCTION: Methamphetamine use remains an important public health concern among young people across various international settings. The present study is aimed at investigating the correlates of methamphetamine use among young Iranians within the general population. MATERIALS AND METHODS: This study was carried out in 13 provinces of Iran in 2013. Through multistage sampling, 3,246 young adults (aged 19-29 years) were recruited in the study. Weighted multilevel logistic regression methods were applied to identify the correlates of methamphetamine use. RESULTS: The lifetime prevalence of methamphetamine use was 7.1% (95% Confidence Interval (CI): 5.4, 8.8). In the multivariable logistic regression, gender (Adjusted Odds Ratio (AOR): 2.57, 95%CI: 1.37, 4.82), marital status (AOR: 4.91, 95%CI: 2.26, 10.7), education level (AOR: 2.56, 95%CI: 1.3, 5.06), profession (AOR: 2.64, 95%CI: 1.63, 4.29), overall knowledge level of methamphetamine use (AOR: 0.55, 95%CI: 0.39, 0.76), knowing a methamphetamine user among family members or friends (AOR: 2.57, 95%CI: 1.71, 4.42), knowing an ecstasy user among family members or friends (AOR: 3.36, 95%CI: 1.92, 5.9), and extramarital sex (AOR: 6.29, 95%CI: 4.29, 9.22) were significantly associated with methamphetamine use. CONCLUSIONS: The lifetime prevalence of methamphetamine use among young Iranian adults is concerning. Educational settings should be equipped with the required resources to take a proactive role in educating adolescents and young adults on substance use including methamphetamine. SCIENTIFIC SIGNIFICANCE: This study was done on a national level and identified the factors that can correlate with methamphetamine use. Its results can be very useful for policy decision makers. (Am J Addict 2017;26:731-737).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".