The prevalence and treatment utilization of substance use disorders among Muslims in the United States: A national epidemiological survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Research on substance use disorder (SUD) among Muslims in the United States (US) is limited. There are several unique factors, including denial and stigma, that make this population at risk of SUD. This study explored the prevalence, treatment utilization, and impact of SUD among Muslims in the US compared with a matched control group from general respondents. METHODS: Data from 372 self-identified Muslims were obtained from the National Epidemiologic Survey on Alcohol and Related Conditions III. A matched non-Muslim control group (N = 744) were selected based on demographics and other SUD-related clinical variables. The impact of SUD was assessed using the 12-Item Short Form Health Survey (SF-12). RESULTS: Among the 372 Muslims, 53 (10.85%) had lifetime alcohol/drug use disorder, while 75 (18.42%) had lifetime tobacco use disorder (TUD). With statistical significance, alcohol use disorder (AUD) was lower while TUD was higher in the Muslim group than in the control group. The rates of all other substances were not statistically different between the Muslim and control groups. The Muslim group have higher help-seeking and a lower mean score on the SF-12 emotional scale than the control group. CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Muslim Americans have higher prevalence of TUD, lower prevalence of AUD, and similar prevalence of other SUD compared to the public. Affected individuals have poor emotional functioning which may be exacerbated by the impact of stigma. This is the first study to estimate prevalence of variety of SUD in American Muslims from a national representative sample.
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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 | 0.000 |
| 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".