WHO-ERS train the trainer in smoking cessation: Three years' experience
Bibliographic record
Abstract
WHO-ERS Train the Trainer (TTT) in smoking Cessation (SC) is a successive 3year collaboration (2016- 19) to support WHO’s training activities for building capacity of WHO Member States to provide SC interventions (SCIs) to smokers both in hospital and primary care settings. 5 countries with high prevalence of tobacco use and low availability of SC support were selected (Greece, Moldova, Bangladesh, Ecuador, Nigeria). 1) A national training network capable of further training HCPs was established to provide brief TCIs to respiratory patients, 2) an online 6module training course on brief TCIs for HCPs was developed in English. The TTT workshops were jointly conducted by WHO and ERS experts as 30 HCPs were trained on brief SCIs in each country and subsequently endorsed to train others through structured cascading training programs. Consequently, the national training teams conducted 4-6 training workshops for primary care providers (PCP) from 2-4 cities in each country. All trained PCPs were supported to deliver 5As and 5Rs brief SCIs. More than 12.000 smokers were enrolled and given brief advice on quitting as existing training centres for on-the-job training of PCPs was strengthened in all countries to train PCPs on brief TCIs. Success quitting rates at 1m. were (37.1%, 31.7%, 48.4%and 60% respectively) although from unexperienced HCPs. We stress the example of Ecuador where quitting rates at 4 months was 57.2% and at 6 m. 48.9%. The WHO–ERS SC TTT is a concrete example of how international cooperation is essential for building capacity of WHO Member States’ health systems to promote one of the wide-reach approaches to SC: brief SCIs as part of HCPs’ routine practice.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".