Food insecurity (hunger) and fast-food consumption among 180 164 adolescents aged 12–15 years from sixty-eight countries
Bibliographic record
Abstract
Food insecurity has been shown to be associated with fast-food consumption. However, to date, studies on this specific topic are scarce. Therefore, the aim of the present study was to investigate the association between food insecurity and fast-food consumption in adolescents aged 12-15 years from sixty-eight countries (seven low-income, twenty-seven lower middle-income, twenty upper middle-income, fourteen high-income countries). Cross-sectional, school-based data from the Global School-based Student Health Survey were analysed. Data on past 30-d food insecurity (hunger) and fast-food consumption in the past 7 d were collected. Multivariable logistic regression and meta-analysis were conducted to assess associations. Models were adjusted for age, sex and BMI. There were 180 164 adolescents aged 12-15 years (mean age 13·8 (sd 1·0) years; 50·8 % boys) included in the analysis. Overall, severe food insecurity (i.e. hungry because there was not enough food in home most of the time or always) was associated with 1·17 (95 % CI 1·08, 1·26) times higher odds for fast-food consumption. The estimates pooled by country-income levels were significant in low-income countries (adjusted OR (aOR) = 1·30; 95 % CI 1·05, 1·60), lower middle-income countries (aOR = 1·15; 95 % CI 1·02, 1·29) and upper middle-income countries (aOR = 1·26; 95 % CI 1·07, 1·49), but not in high-income countries (aOR = 1·04; 95 % CI 0·88, 1·23). The mere co-occurrence of food insecurity and fast-food consumption is of public health importance. To tackle this issue, a strong governmental and societal approach is required to utilise effective methods as demonstrated in some high-income countries such as the implementation of food banks and the adoption of free school meals.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".