Effectiveness and cost-effectiveness of an intensive and abbreviated individualized smoking cessation program delivered by pharmacists: A pragmatic, mixed-method, randomized trial
Bibliographic record
Abstract
Background: Tobacco use is the leading preventable cause of morbidity and mortality in Canada. Smoking cessation programs (SCPs) that are effective, cost-effective and widely available are needed to help smokers quit. Pharmacists are uniquely positioned to provide such services. This study compares the abstinence rates between 2 pharmacist-led SCPs and the cost-effectiveness between these and a comparator group. The study was conducted in St. John's, Newfoundland and Labrador. Methods: This pragmatic, mixed-method trial randomized smokers to either an existing intensive SCP or a new abbreviated SCP designed for community pharmacies. The primary outcome was 6-month abstinence rates. Cost-effectiveness was determined using abstinence rates for the SCPs and a comparator group. Incremental costs per additional quit were calculated for the trial duration, and incremental costs per life-year gained were estimated over a lifetime. Results: = 0.199). Incremental costs per life-year gained for the SCPs were $1576 (intensive) and $1836 (abbreviated). The incremental costs per additional quit, relative to the comparator group, for the SCPs were $1217 (intensive) and $1420 (abbreviated). Discussion: Both SCPs helped smokers quit, and quit rates exceeded those reported for a comparator group that included a general population of adult smokers (~7%). The incremental costs per additional quit for both SCPs compare favourably to those reported for other initiatives such as quit lines and hospital-based interventions. Conclusion: Pharmacist-led smoking cessation programs are effective and highly cost-effective. Widespread implementation, facilitated by remuneration, has potential to lower smoking prevalence and associated costs and harms.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".