Staff and Patient Attitudes and Issues Related to the Implementaion of a Comprehensive Smoking Ban in a Psychiatic Facility
Bibliographic record
Abstract
Aims: The purpose of the study was to evaluate issues related to the implementation of a smoking ban with the aim of improving programs to help patients reduce or quit smoking. Method: At a 410 bed pyschiatric facility, 73 staff and 68 patients completed separate surveys regarding their attitudes towards a smoke-free policy banning all indoor smoking and smoking on the grounds. Results: 55.2% of patient respondents smoked cigarettes and 42% of participants who smoked have either reduced tobacco consumption or have quit. Changes in quality of life that were found to be significant were mood, eating habits, outlook on life, and level of restlessness, anxiety, stress, and concentration. Comfort level and alertness did not change significantly. These symptoms are all associated with nicotine withdrawal. Nicotine replacement therapy (NRT) was percieved as being effective in relieving nicotine withdrawal symptoms. Over half of the patients have not discussed tobacco use with a health care professional. Staff expressed mixed attitudes towards the ban and the use of NRTs. Concerns were raised over patients smoking while using NRT in fear of the patient recieving too much nicotine. Conclusion: Given that the deleterious health consequences of smoking are well known and given the high prevalence of smoking in the psychiatric population, extraordinary efforts are required to significantly reduce tobacco consumption. The implication of this research is that education on tobacco reduction options ought to be provided to staff as well as patients. Efforts to reduce tobacco usuage must be incorporated into standard patient care.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".