Country and gender differences in the association between violence and cigarette smoking among youth
Bibliographic record
Abstract
BACKGROUND: Exposure to violence in youth may be associated with substance use and other adverse health effects. This study examined cigarette smoking in two middle-income areas with different levels and types of exposure to violence. METHODS: Association of exposure to verbal and physical violence with cigarette smoking in the West Bank oPt (2008) and in Jujuy Argentina (2006) was examined using cross-sectional surveys of 14 to 17-year old youth in 7th to 10th grade using probabilistic sampling. RESULTS: Violence exposure rates were more than double for Palestinian girls (99.6% vs. 41.2%) and boys (98.7% vs. 41.1%) compared with Argentinians. The rate of current cigarette smoking was significantly higher among Argentinian girls compared with Palestinian girls (33.1% vs. 7.1%, p < 0.001). Exposure to verbal violence from family and to physical violence increased the odds of current cigarette smoking, respectively, among Argentinian girls (aOR = 1.3, 95% CI = 1.0-1.7; aOR = 2.5, 95%CI = 1.7-3.8), Palestinian girls (aOR 2.2, 95%CI = 1.1-2.4; aOR = 2.0, 95%CI = 1.1-3.6) and Argentinian boys (aOR = 1.5, 95%CI = 1.1-2.0; aOR = 2.2, 95%CI = 1.6-3.0), but not among Palestinian boys. CONCLUSION: Findings highlight the importance of producing context and gender specific evidence from exposure to violence, to inform and increase the impact of targeted smoking prevention strategies.
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 | 0.001 | 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) | 0.002 | 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".