Investigation About the Smoking Behavior and the Influencing Factors of Floating Children in Urban Public Schools
Bibliographic record
Abstract
Objective To know the smoking status of floating children in urban public schools, to explore the smoking factors of floating children and to provide a scientific theoretical basis of smoking for the prevention and control. Methods Totally 997 children of floating population were randomly selected from five public schools in Chengdu to complete the questionnaire survey about smoking status. Results About 13.9% of children of floating population smoked. Smoking behavior of males and females was significant different, and among girls who had smoked one cigarette accounted for 94.4%, 25.4 percentage points higher than boys. The children of floating population in different grades had a significant difference behavior of smoking, the proportional of students who had smoked in junior third grade was up to one quarter. After multivariate Logistic analysis, gender, grade, academic achievement, the parent-child relationships, smoking persons looked mature, and whether the schools enforce the smoking ban, Internet time and the frequency of entering the electronic game rooms, etc. Those 8 factors were the major factors in smoking behavior of children of floating population. Conclusion Student tobacco control needs the endorsement of family, school, society and many other holistic interventions, then a strong tobacco control network can be formed.
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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 | 0.001 | 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".