The role of psychiatrists and tobacco dependence treatment
Bibliographic record
Abstract
Objective: Global tobacco control efforts in both prevention and treatment have advanced to levels never imagined 20 years ago. This review examines the relationship between mental illness and tobacco use, with particular focus on the role of psychiatrists in the treatment of tobacco dependence. Methods: The literature search utilised MEDLINE, Embase and PsychINFO databases using the terms psychiatry, psychiatrist, smoking cessation, tobacco use disorder and tobacco dependence treatment. A manual search of all references from relevant scientific articles obtained was also conducted. Finally, further material sourced included all major guidelines for smoking cessation or tobacco dependence treatment from the United States, United Kingdom, Canada, Australia and New Zealand. Results: Psychiatry has ignored tobacco dependence and its treatment resulting in multiple missed opportunities in improving the health and well-being of smokers with mental illness. Improvement in the training and knowledge of psychiatrists and those in the mental health sector will be the most effective activity to rectify this situation. Conclusion: Psychiatry must recognise tobacco dependence as equally important as the primary mental illness and to treat accordingly. A significant change in the training of future psychiatrists, introducing or implementing smoke free mental health services, changes in the management of caring for the mentally ill, and the introduction of tobacco treatment specialists within the mental health system is needed if psychiatry is serious about confronting this problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".