Prevalence of Nicotine Dependence Among Industrial Workers in Myanmar
Bibliographic record
Abstract
Smoking is one of the major public health concerns and it is harmful to people’s health status. This cross sectional survey was conducted to study the Nicotine dependence among industrial workers in Myanmar. Mandalay city was purposively selected where the second largest industrial zone is situated. A total of two hundred and ninety two industrial workers aged sixteen years old and above participated. Data collection was done by using an interviewer administered questionnaire. Data analysis was done by using SPSS version 23 and descriptive findings were interpreted as frequency and percentage, and the chi-squared test was used to find the association of nicotine dependence. The significance level of all statistical tests was determined with the p value < 0.05. All of the respondents were male (100%) and the mean age of the respondents was 30.7. About 75% of the industrial workers showed low and very low dependence from the nicotine while the rest 25% showed medium to very high dependence. There was a statistically significance association between age (p < 0.001), marital status (p = 0.031), education status (p < 0.001), income (p < 0.001), age at first cigarette smoked (p < 0.001) and number of years cigarette smoked (p < 0.015) and nicotine dependence. Further studies with similar setting are recommended to conduct on nicotine dependence and its correlation among the industrial workers in Myanmar, and the government authority should plan and conduct the effective intervention on health education and awareness program about smoking and nicotine dependence.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".