Tobacco use and cessation counselling: cross-country. Data from the Global Health Professions Student Survey (GHPSS), 2005–7
Bibliographic record
Abstract
BACKGROUND: Brief intervention by a health professional can substantially increase smoking cessation rates among patients. However, few studies have collected information on tobacco use and training to provide cessation counselling among health professional students. OBJECTIVE: To examine tobacco use prevalence and tobacco cessation training among students pursuing advanced degrees in health professions. METHODS: The Global Health Professions Student Survey (GHPSS) has been conducted among third-year students attending dental, medical, nursing and pharmacy schools. The GHPSS was conducted in schools during regular lectures and class sessions. GHPSS follows an anonymous, self-administered format for data collection. RESULTS: The GHPSS was completed by at least one of the four target disciplines in 31 countries between 2005 and 2007 for a total of 80 survey sites. In 47 of the 80 sites, over 20% of the students currently smoked cigarettes; and in 29 of 77 sites, over 10% of the students currently used other tobacco products. GHPSS data showed that the majority of health professional students recognised that they are role models in society, believed that they should receive training on counselling patients to quit using tobacco, but in 73 of 80 sites less than 40% of the students reported they received such training. CONCLUSIONS: Health professional schools, public health organisations and education officials should discourage tobacco use among health professionals and work together to design and implement programmes that train all health professionals in effective cessation counselling techniques. If the goal of the tobacco control community is to reduce substantially the use of tobacco products, then resources should be invested in improving the quality of education of health professionals with respect to tobacco control.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".