What are the primary biopsychosocial and structural factors contributing to youth substance use initiation?
Bibliographic record
Abstract
Canada is facing a national public health emergency due to a significant rise in substance-related overdoses, with illicit drug overdoses now the leading cause of death among youth aged 10 to 18 in Western Canada. Understanding risk and protective factors that contribute to youth substance initiation can help identify interventions focused on prevention and harm reduction. The biopsychosocial model of addiction can be used to understand these risk and protective factors. This framework addresses the interconnected influences of biological, psychological, and social factors on a youth’s susceptibility to substance use initiation. Guided by Arksey and O’Malley’s methodological framework and in consultation with a librarian, multiple databases will be searched and articles will be screened using Covidence. Data will be extracted on individual and contextual factors shaping youth vulnerability to alcohol and nicotine (vaping and smoking), including social, cultural, structural, and psychological dimensions, with attention to differences by substance and vulnerability subgroup. A descriptive analysis will explore patterns across substances and vulnerability subgroups, providing a comprehensive overview to inform the subsequent qualitative interviews and survey development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".