Associations of tobacco use and consumption with rurality among patients with psychiatric disorders: Does smoke‐free policy matter?
Bibliographic record
Abstract
PURPOSE: People with psychiatric disorders (PDs) have high risks for tobacco use and associated health effects; however, little is known about differences in tobacco use status and consumption by urban or rural residence. Among patients with PDs, we examined the association of smoke-free policy on tobacco use by rural/urban residence METHOD: A cross-sectional retrospective study (N = 2060) among patients in a psychiatric facility was conducted. Multi-logistic and multilinear regression analyses assessed differences in outcomes stratified by rural/urban status. RESULTS: Among rural residents, a substance use history (odds ratios [ORs[ = 2.82, 95% CI: 2.01-3.96), high school education (OR = 0.71, 95% CI: 0.51-0.98), older age (OR = 0.99, 95% CI: 0.98-1.00), and longer length of hospital stay (OR = 0.99, 95% CI: 0.98-1.00) were associated with tobacco use. Among urban residents, male sex (OR = 1.38, 95% CI: 1.02-1.86), a substance use history (OR = 2.61, 95% CI: 1.86-3.66), and externalizing disorder diagnosis (OR = 2.72, 95% CI: 1.35-5.48) correlated with tobacco use. Increased tobacco consumption among rural residents was associated with being male (β = 0.12, p = 0.007) and having less than a high school education (β = 0.14, P = 0.001). Whereas, White ethnicity (β = 0.14, p = 0.006), having less than a high school education (β = 0.11, p = 0.022), and a psychotic disorder diagnosis (β = 0.25, p = 0.038) were associated with greater tobacco consumption in urban residents. Smoke-free policy was not associated with tobacco use (OR = 1.08, 95% CI: 0.87-1.34) and consumption (β = 0.05, p = 0.134). CONCLUSIONS: Despite higher rates of tobacco use among rural patients with PDs, they have similar risk factors as their urban counterparts. However, residing in a location with a smoke-free policy may not contribute to tobacco use behaviors among those with PDs.
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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 | 0.003 |
| 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) | 0.002 | 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".