Tobacco Smoking within Psychiatric Inpatient Settings: Biopsychosocial Perspective
Bibliographic record
Abstract
Tobacco smoking remains a neglected issue within general psychiatry despite high rates of associated morbidity and mortality. While there has been a coordinated community campaign to reduce tobacco smoking within the general population, mental health facilities have been reluctant to adopt such regulatory controls, and rarely target smoking prevention or treatment. This paper summarizes and discusses evidence relating to the clinical impact of tobacco smoking within inpatient psychiatric settings. A selective review of psychiatric and psychological research on smoking within inpatient settings was conducted, with a particular focus on the influence of smoking on the physical and mental health, pharmacotherapy, and social interactions of patients during their inpatient stay. Patients frequently alter their smoking habits during inpatient treatment, which can affect both their presentation and pharmacotherapeutic management. Smoking also appears to play a central role in social interactions on the ward, with staff frequently using cigarettes to reinforce certain behaviours. Despite current guidelines, mental health professionals rarely address nicotine use among their patients. Nevertheless, programmes that assist patients to quit during an inpatient stay have been shown to be both efficacious and cost-effective. Strategies that address staff concerns and assist in the implementation of effective smoking bans on psychiatric units are also available. Cessation should be a key component of inpatient treatment planning because this setting provides a safe and timely opportunity to help patients quit. A flowchart of interventions that could be incorporated within standard inpatient settings is proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".