Secondhand Smoke Exposure of Expectant Mothers in China: Factoring in the Role of Culture in Data Collection
Bibliographic record
Abstract
Cancer is the leading cause of death worldwide. Tobacco smoking, including secondhand smoking, causes cancer and is responsible for over 22% of global cancer deaths. The adverse impacts of secondhand smoke are more pronounced for expectant mothers, and can deteriorate both mothers' and infants' health and well-being. Research suggests that secondhand smoke significantly increases expectant mothers' risk of miscarriage, cancer, and other chronic disease conditions, and exposes their unborn babies to an increased likelihood of having life-long poor health. In China, a pregnant woman's family members, such as her husband, parents, or in-laws, are the most likely people to be smoking around her. Due to traditional Chinese cultural practices, even though some expectant mothers understand the harm of secondhand smoke, they may be reluctant to report their family members' smoking behaviors. Resulting in severe underreporting, this compromises health experts' ability to understand the severity of the issue. This paper proposes a novel approach to measure secondhand smoke exposure of pregnant women in the Chinese context. The proposed system could act as a stepping stone that inspires creative methods to help researchers more accurately measure secondhand smoking rates of expectant mothers in China. This, in turn, could help health experts better establish cancer control measures for expectant mothers and decrease their cancer risk.
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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 | 0.045 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".