An Epidemiological Study of Substance Use Disorders Among Emerging and Young Adults
Bibliographic record
Abstract
OBJECTIVES: We investigated the prevalence of substance use disorders (SUDs) among emerging adults and quantified the extent to which emerging adults, compared with young adults, have increased odds for SUDs. METHODS: Data were from the 2012 Canadian Community Health Survey-Mental Health (CCHS-MH). Respondents were 15 to 39 y of age ( n = 9228) and were categorized as: early emerging adults (15 to 22 y); late emerging adults (23 to 29 y); and, young adults (30 to 39 y). SUDs [alcohol or drug abuse/dependence (AAD or DAD)] were measured using the WHO Composite International Diagnostic Interview 3.0. The prevalence of SUDs was compared across age groups, and odds ratios (OR) and 95% confidence intervals (CI) were computed from logistic regression models adjusting for sociodemographic and health covariates. Analyses were weighted to maintain representativeness to the Canadian population. RESULTS: The prevalence of AAD was 8.0%, 6.6%, and 2.7% for early emerging adults, late emerging adults, and young adults, respectively. For DAD, the prevalence was 6.4%, 3.6%, and 1.3%. After covariate adjustment, early and late emerging adults had greater odds of reporting AAD (OR = 3.2, 95% CI = 2.2 to 4.9 and OR = 2.4, 95% CI = 1.6 to 3.4, respectively) or DAD (OR = 4.2, 95% CI = 2.5 to 7.0 and OR = 2.5, 95% CI = 1.6 to 4.1, respectively) compared with young adults. Differences between early and late emerging adults were not significant. CONCLUSION: Emerging adults are at increased odds for SUDs. Lack of differences between early and late emerging adults provide evidence of the extension of emerging adulthood into the late 20s. Findings have implications for the provision of screening and treatment of SUDs during this developmental period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".