Association Between Area‐Level Income Inequality and Health‐Related School Absenteeism: Evidence From the <scp>COMPASS</scp> Study
Bibliographic record
Abstract
BACKGROUND: Income inequality is theorized to impact health. However, evidence among adolescents is limited. This study examined the association between income inequality and health-related school absenteeism (HRSA) in adolescents. METHODS: Participants were adolescents (n = 74,501) attending secondary schools (n = 136) that participated in the 2018-2019 wave of the COMPASS study. Chronic (missing ≥3 days of school in the previous 4 weeks) and problematic (missing ≥11 days of school in the previous 4 weeks) HRSA was self-reported. Income inequality was assessed via the Gini coefficient at the census division (CD) level. Multilevel modeling was used. RESULTS: Greater income inequality was associated with a higher likelihood of chronic and problematic HRSA (chronic: OR = 1.17, 95% CI: 1.06, 1.30; problematic: OR = 1.29, 95% CI 1.11 to 1.50). Increased predicted probabilities for Problematic HRSA were observed at greater degrees of income inequality among students who identified as either white, black, Latinx, or mixed, while protective associations were observed among students who identified as Asian or other. No associations were modified by gender. CONCLUSION: Income inequality demonstrated unfavorable associations with HRSA, which was modified by racial identity.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".