Socioeconomic inequality in the risk of intentional injuries among adolescents: a cross-sectional analysis of 89 countries
Bibliographic record
Abstract
INTRODUCTION: In 2015, the elimination of hunger worldwide by 2030 was declared by the United Nations as a Sustainable Development Goal. However, food insecurity remains pervasive, contributing to socioeconomic health inequalities. The overall objective was to investigate the relationship between food insecurity and intentional injuries among adolescents. METHODS: Individual-level data from the Global School-based Student Health Survey from 89 countries were used (370 719 adolescents, aged 12-17 years). Multilevel logistic regressions were used to examine the sex-specific association between the level of food insecurity (none, medium and high) and intentional injuries (interpersonal violence and suicide attempts), accounting for the clustering of observations within surveys. Random-effects meta-analyses were used to analyse dose-response relationships. RESULTS: Medium and high food insecurity were associated with increased odds of reporting an injury from interpersonal violence among both sexes. A positive dose-response relationship was found, where each level increase in food insecurity was associated with a 30% increase in the odds of an injury due to interpersonal violence among boys (1.30; 95% CI 1.26 to 1.34) and a 50% increase among girls (1.53; 95% CI 1.46-1.62). The odds for suicide attempts increased by 30% for both sexes with each level increase in food insecurity (boys: 1.29; 95% CI 1.25-1.32; girls: 1.29; 95% CI 1.25-1.32). DISCUSSION: The findings indicate that socioeconomic inequalities exist in the risk of intentional injuries among adolescents. Although additional studies are needed to establish causality, the present study suggests that the amelioration of food insecurity could have implications beyond the prevention of its direct consequences.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".