Trends in gender and socioeconomic inequalities in adolescent health over 16 years (2002–2018): findings from the Canadian Health Behaviour in School-aged Children study
Bibliographic record
Abstract
INTRODUCTION: Monitoring health inequalities in adolescents informs policy approaches to reducing these inequalities early in the life course. The purpose of this study was to investigate trends in gender and socioeconomic inequalities in six health domains. METHODS: Data were from five quadrennial survey cycles of the Health Behaviour in School-aged Children (HBSC) study in Canada (pooled n = 94 887 participants). Differences in health between socioeconomic groups (based on material deprivation) and between genders were assessed using slope and relative indices of inequality in six health domains: daily physical activity, excess body weight, frequent physical symptoms, frequent psychological symptoms, low life satisfaction, and fair or poor self-rated health. RESULTS: Over a 16-year period, adolescents in Canada reported progressively worse health in four health domains, with those at the lowest socioeconomic position showing the steepest declines. Socioeconomic differences increased in excess body weight, physical symptoms, low life satisfaction, and fair or poor health. Gender differences also increased. Females showed poorer health than males in all domains except excess body weight, and gender differences increased over time in physical symptoms, psychological symptoms and low life satisfaction. CONCLUSION: Socioeconomic and gender inequalities in health are persistent and widening among adolescents in Canada. Policies that address material and social factors that contribute to health disparities in adolescence are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".