Social inequality in adolescents' healthy food intake: the interplay between economic, social and cultural capital
Bibliographic record
Abstract
Background: Current explanations of health inequalities in adolescents focus on behavourial and economic determinants and rarely include more meaningful forms of economic, cultural, and social capital. The aim of the study was to investigate how the interplay between capitals constitutes social inequalities in adolescent healthy food intake. Methods: Data were collected in the 2013/14 Flemish Health Behavior among School-aged Children (HBSC) survey, which is part of the international WHO HBSC survey. The total sample included 7266 adolescents aged 12-18. A comprehensive set of 58 capital indicators was used to measure economic, cultural and social capital and a healthy food index was computed from a 17-item food frequency questionnaire (FFQ) to assess the consumption frequency of healthy food within the overall food intake. Results: The different forms of capital were unequally distributed in accordance with the subdivisions within the education system. Only half of the capital indicators positively related to healthy food intake, and instead 17 interactions were found that both increased or reduced inequalities. Cultural capital was a crucial component for explaining inequalities such that social gradients in healthy food intake increased when adolescents participated in elite cultural practices ( P < 0.05), and were consequently reduced when adolescents reported to have a high number of books at home ( P < 0.05). Conclusion: A combination of selected resources in the form of economic, cultural and social capital may both increase or reduce healthy food intake inequalities in adolescents. Policy action needs to take into account the unequal distribution of these resources within the education system.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".