Mapping Oral Health and Tobacco Risk Profiles Among Incarcerated Populations in a Central Prison of Navi Mumbai Using a Novel TRACE Framework—A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: Tobacco-related habits, including both smoked and smokeless forms, remain a public health concern among incarcerated populations, where stress, stigma, and limited healthcare access contribute to high prevalence rates. This cross-sectional study was conducted among inmates in a central prison in Navi Mumbai, India and aimed to evaluate tobacco-use patterns, cessation motivation, and oral health outcomes among prison inmates in Navi Mumbai. METHODS: A total population sampling technique was employed, which included 3321 out of 3333 inmates with varying durations of incarceration. Data were collected using a novel TRACE (Tobacco Use, Risk Factors, Assessment, Cessation, and Effects through Epidemiology) framework, incorporating the MTSS (Motivation to Stop Scale) and clinical assessments using the DMFT (Decayed, Missing, and Filled Teeth) index and OHI-S (Oral Hygiene Index-Simplified). Statistical analysis was performed using SPSS version 21 to explore associations between tobacco use and oral health outcomes in this vulnerable population. RESULTS: < 0.001). Oral mucosal lesions were observed in 2.6% of inmates. While 76.3% of tobacco users expressed willingness to quit, access to cessation support remained minimal. CONCLUSION: These findings underscore the need for targeted interventions, such as in-house tobacco cessation programs and oral health services, in correctional facilities. Integrating cessation counseling into prison healthcare policies could improve outcomes among incarcerated populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".