Prevalence, environmental exposure towards tobacco use among health professions’ students
Bibliographic record
Abstract
Tobacco is the world’s major preventable killer. The world is in a state of tobacco epidemic, with larger population of tobacco users. According to WHO, tobacco is the second major cause of death in the world. It is currently responsible for the death of one in ten adults worldwide. It has been predictable that the death tolls will reach, to 10 million by 2020, out of which 70 per cent will occur in the developing countries. The health professionals take part in considerable roles in tobacco control, their attitude and practice toward tobacco use can influence the health of the society. The present study aimed to assess Prevalence and exposure to environmental tobacco use, among the Health professions’ students. A cross sectional survey conducted among nursing and pharmacy students. The study used questionnaire from Global Health professions’Students Survey (GHPSS) which was developed by the World Health Organization, US Center for Disease Control and the Canadian Public Health Association (2008).Descriptive research design was adapted for the present study. One Hundred and forty one subjects (Nursing (n=62) and Pharmacy (n=79) college students) were selected through purposive sampling method. The self administered questionnaire was distributed and data were collected for socio demographic characteristics and Prevalence of tobacco use and exposure to environmental tobacco use. The collected data were systematically coded, computed and analyzed using SPSS 21.0. Analyses of the data were done by authors in accordance with the specified objectives. Tobacco smoking prevalence among health professions’ students are relatively low; however, majority believed that health-care providers serve as role models for their patients and the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".