The Impact of a Smoking Cessation Policy on Visits to a Psychiatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: Smoking cessation policies are increasingly imposed in mental health facilities because of the high prevalence of tobacco smoking and its related adverse health consequences. The objective of this study was to measure the impact of 2 smoking cessation policies--one imposed in a specific psychiatric hospital and the other across the entire province of Ontario--on weekly visit rates to a psychiatric emergency department. METHODS: Administrative data records from consecutive patient visits to a psychiatric emergency department were grouped by week from March 1, 2002, to December 31, 2005. The patients were grouped into 3 broad diagnostic categories: substance-related disorders, psychotic disorders, and other disorders. The impact of 2 smoking cessation policies--one imposed on September 21, 2005 at the Centre for Addiction and Mental Health (CAMH) and one imposed on May 31, 2006 across the province of Ontario--on psychiatric emergency department visit rates was measured using time series analysis. RESULTS: The CAMH-specific smoking cessation policy had no impact on psychiatric emergency department visit rates in any diagnostic category. The province-wide smoking cessation policy resulted in a 15.5% reduction in patient visits for patients with a primary diagnosis of psychotic disorder. CONCLUSIONS: The benefits of a smoking cessation policy need to be balanced by the impact of the policy on the likelihood of patients to seek treatment.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".