Knowledge, Attitude and Practice of Smoking Cessation Advice among Dental Students in Delhi-NCR
Bibliographic record
Abstract
INTRODUCTION: The problem of tobacco abuse is not recent and has been documented well in history in various cultures all over the world. Dental colleges house vast number of potential tobacco cessation counsellors as budding health professionals who are in direct contact with patients. AIM: The main objective of present study was to assess the knowledge, attitude and practice of smoking cessation advice among dental students in Delhi-NCR. MATERIALS AND METHOD: A cross sectional study was conducted among 2953 undergraduate clinical students of third year, fourth year and Internship were enrolled in the study from 18 dental colleges in Delhi- National Capital Region (NCR) using a pre-tested, self-administered questionnaire was employed as an instrument. The questionnaire’s content, face and criterion validity were checked and reliability was tested using Cronbach’s Alpha and Inter Class Co-relation. Statistical analysis used included quantitative statistics, student t test and ANOVA. RESULTS: Students demonstrated considerable knowledge regarding smoking related policies in the institution, technique and products used for smoking cessation and impact of smoking on oral health, general health and treatments to be performed. However, only half of them had a positive attitude towards tobacco cessation counselling to patients and practice this in the hospital setting. CONCLUSION: Based on the results of the study, there is a need to further motivate students on smoking cessation advice.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".