Is problematic Internet use associated with substance use among youth? A systematic review
Bibliographic record
Abstract
Abstract Background Problematic Internet use is an important emerging public health problem. Among youth, the link between problematic Internet use and other risky behaviors needs to be define. The National Institute on Drug Abuse was recently questioning if this new problem can explain the downward trend in substance use among young people. The objective of the systematic review is to explore the association between Internet use (with an average time measure and a problematic Internet use measure) and psychoactive substance use (alcohol, cannabis) among youth. Methods Empirical studies meeting inclusion criteria were chosen from important databases and then screened. Quality assessment and narrative synthesis were executed giving the high heterogeneity. Forty-three studies were eligible. Results A majority of studies found a positive association for the association between Internet problematic use and alcohol use, and between Internet problematic use and cannabis use. High heterogeneity in the assessment of alcohol and cannabis use made the synthesis a great challenge. Studies with substance use assessment that were reflecting a higher risk measure more often found a positive association. Conclusions Despite the diversity of the measures used, it seems that Internet use has a potential association with alcohol and cannabis use among youth around the world. When addressing risky behavior such as substance use among youth, professionals should also address problematic Internet use. Further studies are needed to assess the longitudinal impact of Internet use on youth substance use. A golden standard on how to assess alcohol and cannabis use among youth would be welcomed and certainly help future knowledge synthesis. Key messages Internet problematic use has a potential positive association with alcohol and cannabis use among youth around the world. Prevention programs for youth addressing risky behavior should include problematic Internet use, an important emerging public health problem.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".