Treating Tobacco Dependency in National Health Service Workers in Greater Manchester: An Evaluation of a Bespoke Digital Service
Bibliographic record
Abstract
Introduction: Treating tobacco dependency in National Health Service (NHS) workers delivers substantial benefits at an individual, population, and health care system level. We report the outcomes from the Greater Manchester Integrated Care Partnership's tobacco dependency treatment program for NHS workers which includes 6-months' access to behavioral support and 12 weeks of treatment through a digital application. Methods: Aggregate results for all participants across the program from January 1, 2022, to September 1, 2023, are reported including a deep-dive evaluation of 300 participants recruited to provide chemically validated outcomes. Results: A total of 1567 NHS workers participated in the program within the evaluation period, completing 24,048 sessions with specialist advisors within the application, ordering 18,710 nicotine vape liquids, 6927 nicotine patches, and 297 short-acting nicotine products. Users reported achieving 89,464 smoke-free days, 1,258,069 less cigarettes smoked, and a financial saving of £622,231. The deep-dive evaluation revealed a CO-verified 12-week abstinence rate of 37% (111 of 300). Conclusion: This evaluation provides assurance of clinical effectiveness within a bespoke digital tobacco dependency treatment program for NHS workers across an Integrated Care Partnership.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".