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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".