Shifting culture and taking action to reduce smoking and premature death among people with a mental health condition
Bibliographic record
Abstract
Purpose Mental health conditions affect almost a quarter of the population who die on average 10-20 years earlier than the general population. Smoking is the single largest cause of this gap in life expectancy. Smoking rates among people with mental health conditions have barely changed over the last 20 years during a time when rates have been steadily falling in the general population. Action is needed to address the growing difference in smoking rates among those with a mental health condition compared to the general population. The paper aims to discuss these issues. Design/methodology/approach This work has been informed by the input of a wide range of experts and professionals from across public health, mental health and the wider NHS. Findings People with a mental health condition are just as likely to want to stop smoking as other smokers but they face more barriers to quitting and are more likely to be dependant and therefore need more support. Quitting smoking does not exacerbate poor mental health; in fact the positive impact of smoking cessation on anxiety and depression appears to be at least as large as antidepressants. Originality/value The full report outlines the high-level ambitions and the specific actions that must be realised to drive down smoking rates among those with a mental health condition.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".