The effectiveness of NHS smoking cessation services: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To analyse evidence on the effectiveness of intensive NHS treatments for smoking cessation in helping smokers to quit. METHODS: A systematic review of studies published between 1990 and 2007. Electronic databases were searched for published studies. Unpublished reports were identified from the national research register and experts. RESULTS: Twenty studies were included. They suggest that intensive NHS treatments for smoking cessation are effective in helping smokers to quit. The national evaluation found 4-week carbon monoxide monitoring validated quit rates of 53%, falling to 15% at 1 year. There is some evidence that group treatment may be more effective than one-to-one treatment, and the impact of 'buddy support' varies based on treatment type. Evidence on the effectiveness of in-patient interventions is currently very limited. Younger smokers, females, pregnant smokers and more deprived smokers appear to have lower short-term quit rates than other groups. CONCLUSION: Further research is needed to determine the most effective models of NHS treatment for smoking cessation and the efficacy of those models with subgroups. Factors such as gender, age, socio-economic status and ethnicity appear to influence outcomes, but a current lack of diversity-specific analysis of results makes it impossible to ascertain the differential impact of intervention types on particular subpopulations.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".