Lifestyle Changes and Passive Smoking Risk in Rural Japan: Comparing Cross-Sectional Surveys
Bibliographic record
Abstract
Recommended lifestyle and good mental/physical status in childhood are factors that facilitate lifelong health. This study investigated changes in the lifestyle of Japanese rural children over the past 15 years during which, tobacco-related education and lifestyle issues have significantly changed. This descriptive cross-sectional study was conducted in 2017 in Town A, rural, Japan. Data were collected using self-administered questionnaires, which were distributed to 360 students of two elementary schools and junior high schools in Town A. Daily activities, passive smoking exposure, mental/physical status, and Internet usage were evaluated and compared to data obtained from a similar survey conducted in 2002 in the same area and schools. Sleep time in 2017 was reduced compared to that in 2002 in both sexes but was significantly reduced only in girls. Exposure to passive smoking in 2017 was reduced compared to that in 2002, consistent with the significant reductions in the presence of family smokers (2002: 70.2%; 2017: 46.9%) and smoking in indoor living areas by family members (2002: 45.7%; 2017: 5.6%). Furthermore, the rate of intended future smoking was significantly lower in 2017 than in 2002 for both boys and girls (p<.001). Of 18 symptoms that can negatively affect students’ physical and mental health status, 12 were significantly reduced in 2017 compared to those in 2002. The lifestyle of children in rural Japan has improved over the past 15 years and children maintain negative attitudes toward future smoking. However, more attention to technology-related lifestyle issues, including watching television and Internet usage, is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".