Smoking Behaviour Among Resident University Students In North India: Some Issues And Challenges
Bibliographic record
Abstract
The use of tobacco as cigarettes has taken a form of epidemic. Unless it is not managed properly and in time it may become an uncontrollable behavior leading to long term health and social problem. This paper is based on a survey that was intended to explore the smoking habits of university students residing in hostels, their perception towards it, factors associated with it and their implications. The study was conducted using a questionnaire based survey among 200 students, who were in the habit of smoking, belonging to different classes and residing in the hostels of a residential central University of North India. It was found that smokers were mainly from urban background. More than a quarter of the smokers spent more than 600 rupees per month on smoking only. The most important reasons given by students for smoking behavior was peer pressure followed by tension. Most of them had started smoking between 14-17 years of age, followed by 17- 21 yrs. age group. The number of cigarettes used increased with seniority. Most of the surveyed students wanted to leave the habit but could not do so because of bad habit followed by tension. 51% faced health problems, the major ones being respiratory problems. The study suggests that most effective control of the habit can be achieved by targeting the students of adolescent age and minimizing the tension among them. Keywords: Smoking, Adolescents, students’ behavior, central university
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".