Race/Ethnicity Inequities in the Association Between Movement Behaviors and Suicidal Thoughts/Ideation Among Adolescents
Bibliographic record
Abstract
OBJECTIVE: We aimed to analyze the associations between movement behaviors (physical activity, screen time, and sleep), independently and jointly, and suicidal thoughts/ideation among Brazilian adolescents according to race/ethnicity. METHODS: This cross-sectional study surveyed 4,081 adolescents aged 15-19 years (49.9% females) across all Brazilian geographic regions. Data were collected using a self-administered questionnaire. Within the sample, 31.0% (n = 1,264) self-reported as White and 69.0% (n = 2,817) as Black. Adolescents who declared one or more times/week suicidal thoughts/ideation were considered as a risk group. Accruing moderate-to-vigorous physical activity during leisure time, reduced recreational screen time, and good sleep quality were the exposures investigated. We evaluated both additive and multiplicative interactions between race/ethnicity and movement behaviors. Binary logistic regression was used to estimate the odds ratio (OR), marginal means effects, and 95% confidence intervals (95% CIs). RESULTS: Black adolescents who met 1 (OR: 0.34; [95% CI: 0.22-0.52]), 2 (OR: 0.17 [0.11-0.27]), or 3 (OR: 0.13 [0.07-0.26]), and White adolescents who met 1 (OR: 0.35 [0.21-0.57]), 2 (OR: 0.14 [0.08-0.26]), or 3 (OR: 0.11 [0.04-0.31]) of the movement behavior targets had lower odds of suicidal thoughts/ideation than Black adolescents who did not meet any of the movement behavior targets. Black adolescents who did not meet any of the movement behavior targets had higher suicidal thoughts/ideation odds than the other adolescent's groups. CONCLUSIONS: We identified an inverse association between meeting individuals and combinations of movement behavior targets with suicidal thoughts/ideation. Among Black adolescents who did not meet any targets, these associations were more evident.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".