Trends in social inequalities in adolescent health complaints from 1994 to 2010 in Europe, North America and Israel: The HBSC study
Bibliographic record
Abstract
BACKGROUND: Studies have shown constant or increasing health inequalities in adulthood in the last decades, but less is known about trends in health inequalities among adolescents. The aim is to analyse changes in socioeconomic differences in subjective health complaints from 1994 to 2010 among 11- to 15-year-olds in Europe, North America and Israel. METHODS: Data were obtained from the international 'Health Behaviour in School-aged Children' (HBSC) survey. Analyses were based on the HBSC surveys conducted in 1994 (19 countries), 1998 (25 countries), 2002 (32 countries), 2006 (37 countries) and 2010 (36 countries) covering a time period of up to 16 years. Log binomial regression models were used to assess inequalities in multiple health complaints. Socioeconomic position was measured using perceived family wealth. RESULTS: Inequalities in multiple health complaints emerged in almost all countries, in particular since 2002 (RR 1.1-1.7). Trend analyses showed stable (29 countries), increased (5 countries), decreased (one country) and no social inequalities (2 countries) in adolescent health complaints. CONCLUSION: In almost all countries, social inequalities in health complaints remained constant over a period of up to 16 years. Our findings suggest a need to intensify efforts in social and health policy to tackle existing inequalities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".