Impact of the ENSP eLearning platform on improvingknowledge, attitudes and self-efficacy for treating tobaccodependence: An assessment across 15 European countries
Bibliographic record
Abstract
INTRODUCTION: In 2018, the European Network for Smoking Cessation and Prevention (ENSP) released an update to its Tobacco Treatment Guidelines for healthcare professionals, which was the scientific base for the development of an accredited eLearning curriculum to train healthcare professionals, available in 14 languages. The aim of this study was to evaluate the effectiveness of ENSP eLearning curriculum in increasing healthcare professionals' knowledge, attitudes, self-efficacy (perceived behavioral control) and intentions in delivering tobacco treatment interventions in their daily clinical routines. METHODS: We conducted a quasi-experimental pre-post design study with 444 healthcare professionals, invited by 20 collaborating institutions from 15 countries (Albania, Armenia, Belgium, Italy, France, Georgia, Greece, Kosovo, Romania, North Macedonia, Russia, Serbia, Slovenia, Spain, Ukraine), which completed the eLearning course between December 2018 and July 2019. RESULTS: Healthcare professionals' self-reported knowledge improved after the completion of each module of the eLearning program. Increases in healthcare professionals' self-efficacy in delivering tobacco treatment interventions (p<0.001) were also documented. Significant improvements were documented in intentions to address tobacco use as a priority, document tobacco use, offer support, provide brief counselling, give written material, discuss available medication, prescribe medication, schedule dedicated appointment to develop a quit plan, and be persistent in addressing tobacco use with the patients (all p<0.001). CONCLUSIONS: An evidence-based digital intervention can be effective in improving knowledge, attitudes, self-efficacy and intentions on future delivery of tobacco-treatment interventions.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".