International trends in ‘bottom-end’ inequality in adolescent physical activity and nutrition: HBSC study 2002–2014
Bibliographic record
Abstract
Background: In spite of many positive trends that have emerged in the health of young people, adolescents from more affluent groups continue to experience more favourable health outcomes. There are no groups that are more vulnerable than those who report very poor ('bottom-end') indicators of health behaviour. The present study investigated the role of socio-economic factors as potential determinants of bottom-end health behaviours pertaining to physical activity and diet. Methods: Our analysis incorporated health data for some 700 000 15-year-old adolescents in 34 countries. The data source was four cycles of the Health Behaviour in School-aged Children (HBSC) study (2001/2002, 2005/2006, 2009/2010 and 2013/2014). As per UNICEF precedents, adolescents whose health behaviour scores were below the mean of the lower half of the distribution fell into the 'bottom-end' on this indicator. Results: Adolescents from less affluent families were much more likely to report being in the bottom-end of the distribution of these health indicators. Large, persistent and widespread socio-economic gradients existed for physical activity and healthy eating, while the findings were mixed for unhealthy eating. Such socio-economic inequalities were largely stable or widened for physical activity and healthy eating, while inequalities in unhealthy eating narrowed. Conclusion: Although it is important to continue monitoring average levels of adolescent health, national and international policies need to pay attention to the concentration of poor health outcomes among adolescents from less affluent families and to redress social inequalities in adolescent health behaviour.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".