Spotlight on adolescent health and well-being. Findings from the 2017/2018 Health Behaviour in School-aged Children (HBSC) survey in Europe and Canada. International report. Volume 1. Key findings
Bibliographic record
Abstract
This report presents key findings from 227 441 young \npeople aged 11, 13 and 15 years in 45 countries/regions \nwho participated in the 2017/2018 Health Behaviour in \nSchool-aged Children (HBSC) survey. The findings highlight \nsome positive trends in relation to adolescents? health \nand well-being. Most adolescents experience positive \nand supportive social relationships, relatively few health \nproblems, and good overall health and well-being. \nSubstance use continues to decline and eating habits \nare improving. Challenges nevertheless remain. \nThere is some evidence of increasing pressure at school, \nespecially among older adolescents, at a time when \nperceived support from family and teachers decreases. \nThe proliferation of digital media has led to problematic \nuse among some adolescents whose social media \nbehaviours affect their relationships with family and friends \nand disrupt other activities. Physical activity levels remain \nextremely low and increasing numbers of young people \nare reporting issues that affect their mental health, such \nas feeling low and sleep difficulties. Persistent social and \ngender inequalities remain, and many aspects of health \nand well-being worsen with age. \nBy helping to make young people?s lives more visible, \nHBSC continues to underpin effective actions to promote \nthe health of adolescents across the WHO European Region, \nCanada and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".