The Effect of Race on Provider Decisions to Test for Illicit Drug Use in The Peripartum Setting
Bibliographic record
Abstract
BACKGROUND: Testing for illicit drugs may expose women who test positive to severe legal and social consequences. It is unknown whether racial disparities in drug testing practices underlie observed disparities in legal and social consequences of positive tests. METHODS: Using administrative hospital and birth certificate data, we analyzed factors associated with both receipt and results of illicit drug testing among women with live births during 2002-2003. We assessed the independent association of race and other sociodemographic factors with both receipt of a drug test by the mother or her newborn infant and positive maternal or neonatal toxicology results, after controlling for obstetrical conditions and birth outcomes associated with maternal substance abuse. RESULTS: Of the 8487 women with live births, 244 mother-newborn pairs (3%) were tested for illicit drug use. Black women and their newborns were 1.5 times more likely to be tested for illicit drugs as nonblack women in multivariable analysis. However, race was not independently associated with a positive result. CONCLUSIONS: We identified racial differences in rates of testing for illicit drug use between black and nonblack women. We found equivalent positivity rates among tested black and nonblack women. The prevalence of drug use among untested women is unknown, however, so although tested women had equivalent rates of substance use detected, whether black and nonblack substance users are equally likely to be identified in the course of peripartum care remains uncertain.
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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 | 0.002 | 0.021 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".