Real life impact of educating nurses in tobacco cessation intervention
Bibliographic record
Abstract
Introduction Nurses, when educated in tobacco cessation interventions, are well positioned to address smoking with their patients achieving a long-time quit rate of approximately 10%, according to the literature. Methods Together in partnership with the International Society of Nurses in Cancer Care and University of California in Los Angeles, USA, and the Society for Treatment of Tobacco Dependence, Czech Republic, an international project funded by Bristol-Myers Squibb Foundation “The Eastern Europe Nurses‘ Centre of Excellence in Tobacco Control – Developing Nurse Champions for Tobacco Dependence Treatment” (EE-COE) involves six Eastern European countries (CZ, HU, MD, RO, SI, SK). The EE-COE offers to nurses various educational activities in tobacco control, i.e. train of trainer workshops, short seminars, or online e-learning. Through these methods positively evaluated in previous projects, thousands of nurses have already been educated. Results Results from EE-COE 2016 five country 3-month post-training online surveys, a total of 507 trained nurses estimated that they offered cessation intervention to 850 to 1239 patients a week (minimum / maximum weekly estimates, respectively), a mean of 1,044 patients per week. Assuming a 10% long-term quit rate from nurses’ intervention, we estimated that 104 patients quit tobacco use per week, or 5,408 ex-smokers per year in five countries alone. Simplified calculation of investment into nurses’ education translated to $63 USD spent per ex-smoker. Conclusion Investment into nurses‘ education in tobacco control knowledge and skills is an effective approach with substantial impact on patients quitting smoking, improved patient health, in addition to health care cost savings.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".