Incarceration and Current Tobacco Smoking Among Black and Caribbean Black Americans in the National Survey of American Life
Bibliographic record
Abstract
OBJECTIVES: We examined the relationship between having a history of incarceration and being a current smoker using a national sample of noninstitutionalized Black adults living in the United States. METHODS: With data from the National Survey of American Life collected between February 2001 and March 2003, we calculated individual propensity scores for having a history of incarceration. To examine the relationship between prior incarceration and current smoking status, we ran gender-specific propensity-matched fitted logistic regression models. RESULTS: A history of incarceration was consistently and independently associated with a higher risk of current tobacco smoking in men and women. Formerly incarcerated Black men had 1.77 times the risk of being a current tobacco smoker than did their counterparts without a history of incarceration (95% confidence interval [CI] = 1.20, 2.61) in the propensity score-matched sample. The results were similar among Black women (prevalence ratio = 1.61; 95% CI = 1.00, 2.57). CONCLUSIONS: Mass incarceration likely contributes to the prevalence of smoking among US Blacks. Future research should explore whether the exclusion of institutionalized populations in national statistics obscures Black-White disparities in tobacco smoking.
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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 | 0.002 |
| 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) | 0.001 | 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".