To Study the Impact of Common Facets of Quality of life in Chronic Tobacco Chewers Using WHOQOL Scale- a Cross Sectional Descriptive Survey
Bibliographic record
Abstract
Tobacco dependence is a well acknowledged social and health evil. In india 8-9lakh persons die every year due to tobacco related disease . Numerous study show that tobacco withdrawl lead to considerable decreasse in health risks caused by tobacco use habit. Moreover there is not muchstudy available on causes and prevalance.• Smokeless tobacco is found to be as addictive and harmful as smoking but have not been explored into, especially among youth.• Tobacco is estimated to have killed 100 million people in the 20th century and continues to kill 5.4 million people every year and this figure is expected to rise to 8 million per year by 2030, 80% of which will occur in the developing countries. Tobacco is used in different forms and the health effects are seen irrespective of the form in which it is used. Smokeless tobacco is found to be as addictive and harmful as smoking yet more difficult to quit. Smokeless tobacco, especially in the form of chewing has been associated with various oral diseases including cancers and adverse tobacco chewing is a habit with adverse consequences ranging from benign oral lesions to cancer. It plagues all countries from highly industrialized ones like Sweden and Canada todeveloping ones. 11.6% of any form of ever tobacco use among youth in Global Youth Tobacco Survey (GYTS) and alsohigher than prevalence found. Many studies regarding the tobacco use, smoking and its harmful effects has been done but very few on smokeless tobacco has done.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".