The contribution of club participation to adolescent health: evidence from six countries
Bibliographic record
Abstract
BACKGROUND: Social networks have been recognised as an important factor for enhancing the health of people and communities. Bridging social capital, characterised by numerous and varied weak ties, exemplifies a particular type of network that can help people reach their goals and improve their health. This study seeks to contribute to the evidence base on the use of positive social networks for young people's health by exploring the importance of club participation in predicting the health and health-related behaviours of 15-year-old girls and boys across Europe and North America. METHODS: Data are derived from a 2005-6 World Health Organization collaborative study, to establish the relationships between different types of club and a range of health outcomes (self-perceived health, wellbeing and symptoms) and health-related behaviours (smoking, drinking). Multi-level logistic regression was used to assess the independent effects of club participation by controlling for gender and socioeconomic position. Data were compared across six countries. RESULTS: All the considered outcomes, both in terms of perceived health and wellbeing and health behaviours were associated with participation in formal associations. The associations are in the expected direction (participation corresponding to better health) except for some particular association types. CONCLUSIONS: Participation in formal associations seems supportive for good health and health behaviours in adolescence, and should be promoted in this age group.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".