Trends and Socioeconomic Correlates of Adolescent Physical Fighting in 30 Countries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: No recent international studies provide evidence about its prevalence, trends, or social determinants of physical fighting in adolescents. We studied cross-national epidemiologic trends over time in the occurrence of frequent physical fighting, demographic variations in reported trends, and national wealth and income inequality as correlates. METHODS: Cross-sectional surveys were administered in school settings in 2002, 2006, and 2010. Participants (N = 493874) included eligible and consenting students aged 11, 13, and 15 years in sampled schools from 30 mainly European and North American countries. Individual measures included engagement in frequent physical fighting, age, gender, participation in multiple risk behaviors, victimization by bullying, and family affluence. Contextual measures included national income inequality, absolute wealth and homicide rates. Temporal measure was survey cycle (year). RESULTS: Frequent physical fighting declined over time in 19 (63%) of 30 countries (from descriptive then multiple Poisson regression analyses). Contextual measures of absolute wealth (relative risk 0.96, 95% confidence interval 0.93-0.99 per 1 SD increase in gross domestic product per capita) but not income inequality (relative risk 1.01, 95% confidence interval 0.98-1.05 per 1 SD increase) related to lower levels of engagement in fighting. Other risk factors identified were male gender, younger age (11 years), multiple risk behaviors, victimization by bullying, and national homicide rates. CONCLUSIONS: Between 2002 and 2010, adolescent physical fighting declined in most countries. Specific groups of adolescents require targeted violence reduction programs. Possible determinants responsible for the observed declines are discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".