Effect of Volunteering on Risk Factors for Cardiovascular Disease in Adolescents
Bibliographic record
Abstract
IMPORTANCE: The idea that individuals who help others incur health benefits themselves suggests a novel approach to improving health while simultaneously promoting greater civic orientation in our society. The present study is the first experimental trial, to our knowledge, of whether regular volunteering can reduce cardiovascular risk factors in adolescents. OBJECTIVE: To test a novel intervention that assigned adolescents to volunteer with elementary school-aged children as a means of improving adolescents' cardiovascular risk profiles. DESIGN: Randomized controlled trial, with measurements taken at baseline and 4 months later (postintervention). SETTING: Urban public high school in western Canada. PARTICIPANTS: One hundred six 10th-grade high school students who were fluent in English and free of chronic illnesses. INTERVENTION: Weekly volunteering with elementary school-aged children for 2 months vs wait-list control group. MAIN OUTCOME MEASURES: Cardiovascular risk markers of C-reactive protein level, interleukin 6 level, total cholesterol level, and body mass index. RESULTS: No statistically significant group differences were found at baseline. Postintervention, adolescents in the intervention group showed significantly lower interleukin 6 levels (log10 mean difference, 0.13; 95% CI, 0.004 to 0.251), cholesterol levels (log10 mean difference, 0.03; 95% CI, 0.003 to 0.059), and body mass index (mean difference, 0.39; 95% CI, 0.07 to 0.71) compared with adolescents in the control group. Effects for C-reactive protein level were marginal (log10 mean difference, 0.13; 95% CI, -0.011 to 0.275). Preliminary analyses within the intervention group suggest that those who increased the most in empathy and altruistic behaviors, and who decreased the most in negative mood, also showed the greatest decreases in cardiovascular risk over time. CONCLUSIONS AND RELEVANCE: Adolescents who volunteer to help others also benefit themselves, suggesting a novel way to improve health. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01698034.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".