Predictors of Substance Use/Misuse in Youth
Bibliographic record
Abstract
Introduction: Substance use/misuse is highly prevalent among youth, which is concerning given the associated adverse outcomes such as psychiatric conditions, interpersonal problems, as well as deficits in brain structure, function and cognition. Considering the rising global burden of disease due to substance use disorders, especially for youth in low- to middle-income countries, it is crucial for strategies enabling the prompt detection and early implementation of effective intervention measures for youth at the highest risk of substance use/misuse to be developed. The identification of predictors or other associated factors of youth substance use/misuse may facilitate the development of such strategies and inform policy or intervention efforts. Thus, this thesis aimed to identify predictors or other associated factors of substance use/misuse among youth in Brazil and across the globe. We also sought to determine the prevalence of underage alcohol use among youth in Brazil, since prior studies on similar topics have predominately been conducted in high-income countries. Results: Various predictors or other associated factors of youth substance use/misuse including sociodemographic and clinical characteristics were identified. Risk factors for underage drinking in a nationally representative population of Brazilian adolescents were having other/no religion, residing in rural areas, depression, tobacco use, and illicit drug use. Alcohol abuse/dependence, tobacco abuse/dependence, manic episode history, suicide risk, and male sex were identified as important predictors of illicit substance abuse/dependence among young adults in Brazil using machine learning techniques. A relatively high prevalence of current underage alcohol use in Brazil of 22.2% was also found. Our comprehensive systematic review and meta-analysis indicated that several different types of childhood maltreatment were predictive of various types of youth substance use/misuse, with the included studies conducted in 22 countries across the globe. Conclusion: Several important sociodemographic and clinical predictors or associated factors of substance use/misuse among youth in Brazil and across the globe were found. The identification of significant predictors can facilitate the prompt detection of youth at the highest risk of substance use/misuse and enhance intervention efforts through the implementation of measures to also prevent these predictors. Therefore, these findings highlight the severity of substance use/misuse among youth and indicate the promising potential of identifying predictors or other associated factors in informing early intervention efforts to prevent or mitigate youth substance use/misuse.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".