Tobacco use and cessation in psychiatric disorders: National Institute of Mental Health report
Bibliographic record
Abstract
The National Institute of Mental Health (NIMH) convened a meeting in September 2005 to review tobacco use and dependence and smoking cessation among those with mental disorders, especially individuals with anxiety disorders, depression, or schizophrenia. Smoking rates are exceptionally high among these individuals and contribute to the high rates of medical morbidity and mortality in these individuals. Numerous biological, psychological, and social factors may explain these high smoking rates, including the lack of smoking cessation treatment in mental health settings. Historically, "self-medication" and "individual rights" have been concerns used to rationalize allowing ongoing tobacco use and limited smoking cessation efforts in many mental health treatment settings. Although research has shown that tobacco use can reduce or ameliorate certain psychiatric symptoms, overreliance on the self-medication hypothesis to explain the high rates of tobacco use in psychiatric populations may result in inadequate attention to other potential explanations for this addictive behavior among those with mental disorders. A more complete understanding of nicotine and tobacco use in psychiatric patients also can lead to new psychiatric treatments and a better understanding of mental illness. Greater collaboration between mental health researchers and nicotine and tobacco researchers is needed to better understand and develop new treatments for cooccurring nicotine dependence and mental illness. Despite an accumulating literature for some specific psychiatric disorders and tobacco use and cessation, many unstudied research questions remain and are a focus and an emphasis of this review.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".