Relationship Between BMI, Body image, and Smoking in Korean Women as Determined by Urine Cotinine: Results of a Nationwide Survey
Bibliographic record
Abstract
BACKGROUND: This study examined the influence of body mass index (BMI), subjective body perception (SBP), and the differences between BMI and SBP influence on smoking among women. METHODS: This study used the Korea National Health and Nutrition Examination Survey IV-2, 3 2008-2009. A urinary cotinine test was administered to 5485 women at least 19 years of age. Individuals whose cotinine level was at least 50 ng/mL were categorized as smokers. A multiple logistic regression analysis was performed to estimate the extent to which body-related variables affect female smoking. RESULTS: Women with a lower BMI who perceived themselves to be normal or very fat were 2.09 times (1.14-3.83) more likely to smoke than women with a normal BMI and SBP. Women who were never married with a low BMI and thin SBP were 3.11 times (1.47-6.55) more likely to smoke than women with a normal BMI and SBP. Married women with a high BMI who considered themselves very fat were 0.63 times (0.43-0.94) less likely to smoke than women with a normal BMI and SBP. In contrast, divorced and widowed women with a low or normal BMI who considered themselves very fat were 26.1 times (1.35-507.3) more likely to smoke. CONCLUSIONS: Discrepancies between the objective physical condition (BMI) and the subjective body image (SBP) influence the female smoking rate. To reduce the number of female smokers, public education on the association between smoking behavior and weight issues is needed, especially among women with low BMI and distorted weight perception.
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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 | 0.000 | 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".