Successes and Challenges of Implementing Tobacco Dependency Treatment in Health Care Institutions in England
Bibliographic record
Abstract
There is a significant body of evidence that delivering tobacco dependency treatment within acute care hospitals can deliver high rates of tobacco abstinence and substantial benefits for both patients and the healthcare system. This evidence has driven a renewed investment in the UK healthcare service to ensure all patients admitted to hospital are provided with evidence-based interventions during admission and after discharge. An early-implementer of this new wave of hospital-based tobacco dependency treatment services is "the CURE project" in Greater Manchester, a region in the North West of England. The CURE project strives to change the culture of a hospital system, to medicalise tobacco dependency and empower front-line hospital staff to deliver an admission bundle of care, including identification of patients that smoke, provision of very brief advice (VBA), protocolised prescription of pharmacotherapy, and opt-out referral to the specialist CURE practitioners. This specialist team provides expert treatment and behaviour change support during the hospital admission and can agree a support package after discharge, with either hospital-led or community-led follow-up. The programme has shown exceptional clinical effectiveness, with 22% of all smokers admitted to hospital abstinent from tobacco at 12 weeks, and exceptional cost-effectiveness with a public value return on investment ratio of GBP 30.49 per GBP 1 invested and a cost per QALY of GBP 487. There have been many challenges in implementing this service, underpinned by the system-wide culture change and ensuring the good communication and engagement of all stakeholders across the complex networks of the tobacco control and healthcare system. The delivery of hospital-based tobacco dependency services across all NHS acute care hospitals represents a substantial step forward in the fight against the tobacco epidemic.
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".