School racial composition and lifetime non-medical use of prescription painkillers: Evidence from the national longitudinal study of adolescent to adult health
Bibliographic record
Abstract
OBJECTIVE: To investigate the possible effects of middle and high school racial composition on later reporting of lifetime non-medical use of prescription painkillers (NMUPP) in young adulthood, and to explore whether there is evidence of variability by individual race/ethnicity in such effects. METHODS: Using data from Wave 1 (1994/5) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we categorized the sample's 52 middle schools and 80 high schools as majority (>50%) non-Hispanic white, majority non-Hispanic black, or neither. We used two-level hierarchical modeling to explore associations between individual- and school-level race at Wave 1 and lifetime prescription painkiller misuse reported at Wave 4. We included a cross-level interaction between individual race and school racial composition to assess variability in school-level associations by race. RESULTS: Overall crude prevalence of lifetime NMUPP in majority white schools (17.9%) was over three times that of prevalence in majority black schools (4.8%), and also higher than prevalence in schools neither predominantly black nor predominantly white (12.4%). Lifetime misuse among blacks in majority white schools was more prevalent (5.2%) than among blacks in black schools (2.8%), as was misuse among whites in white schools (19.3%) compared to their white peers in black schools (15.7%). Two-level random intercept Poisson regression results suggest that attendance in a majority black secondary school lowered a participant's risk of lifetime NMUPP (compared to attending a majority white school: RR=0.66, p = 0.03). Compared to blacks in black schools, blacks in white schools had twice the risk of prescription painkiller misuse (p = 0.004) over a decade later, and whites in white schools had 5.5 times the risk (p = 0.01). The risk ratio comparing whites in black schools to whites in white schools was not significant (RR: 1.30; p = 0.37). CONCLUSIONS: We found evidence of an effect of school racial composition on the risk of misusing prescription painkillers over a decade later, over and above individual race, with higher risk of misuse reported among participants who had attended white schools. Black participants who had attended predominantly white schools were, on average, twice as likely to report lifetime misuse of prescription painkillers compared to blacks who had attended black schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".