A study on prevalence of tobacco consumption in tribal district of Madhya Pradesh
Bibliographic record
Abstract
Background: Tobacco use is one of the common risk factors for major non-communicable diseases. It succumbs half of its users to death. Estimates suggest that tobacco will cause about 150 million deaths in the first quarter of the century and 300 million in the second quarter. Prevalence of tobacco use in rural area is higher than urban area. While there is still paucity of data of tobacco consumption among tribal population. The study aims to determine the prevalence of tobacco consumption and its different modes among tribal population. Methods: A cross-sectional study carried out among 800 study subject 15 years and above of randomly selected villages of Mandla district of M.P., from January 2015 to June 2015 using a pre-designed pre-tested proforma. Results: Tobacco consumption was prevalent among 43.38% of the study subjects with khaini (68.3%) being the most common form of tobacco consumed followed by betel nut (9.5%). Its consumption was significantly associated with gender, age group, educational status and the marital status of the respondents. Conclusions: The prevalence of tobacco use is alarmingly high (43.38%). There is a need to strengthen IEC and Behaviour change communication activities focussing on the hazardous effects of tobacco through health education campaigns is needed in tribal areas.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".