Social network dynamics of tobacco smoking and alcohol use among persons involved with the criminal legal system (PCLS): A modeling study
Bibliographic record
Abstract
Background: Tobacco smoking and alcohol use contribute to a synergy of epidemics (a "syndemic") that disproportionately affects persons involved with the criminal legal system (PCLS) and their social networks. An improved understanding of the complex interrelationships among the factors of the incarceration-tobacco-alcohol syndemic is essential to develop effective reform policies and interventions. However, collecting empirical data on these interrelationships is often hampered due to logistical and ethical challenges. Methods: We developed an agent-based network model (ABNM) to simulate the effects of the incarceration-tobacco-alcohol syndemic in the state of Rhode Island, USA. The model was validated and calibrated using empirical survey and demographic data. Outcomes included current smoking and heavy alcohol use rates in the first year after release among previously incarcerated agents and in their social networks. Results: The model successfully replicated demographic, substance use, and incarceration-related parameters. Simulation results suggest high rates of smoking (approximately 80% currently smoking persons in the first few weeks after release) and heavy alcohol use (approximately 40% current heavy alcohol use rate in the first few weeks after release) among PCLS, especially persons with multiple incarceration events. The model also estimated elevated rates of current smoking and current heavy alcohol use in the direct social contacts of PCLS. Discussion: This ABNM integrates biobehavioral health processes relating to incarceration and substance use. This model can be used as a platform to evaluate the potential impacts of interventions provided to PCLS and their networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".