Global youth vaping and respiratory health: epidemiology, interventions, and policies
Bibliographic record
Abstract
E-cigarette usage (also known as e-cigarettes or vaping products) has increasingly been recognized as a global public health problem. One challenge in particular involves their marketing to minors (teenagers and children) and the rising prevalence of use in this population. E-cigarettes unnecessarily expose minors to health risks, these include respiratory health problems, such as exacerbations of asthma, bronchitis, and respiratory-tract irritation. Nicotine, commonly found in e-cigarettes, is also associated with cognitive impairment and neurodevelopmental problems. E-cigarettes are also risk factors for downstream substance use, including cigarettes and cannabis initiation (the gateway hypothesis), which compounds health risks in dual users. Current public health preventative and intervention studies are limited, and there is a clear need for more interventions that may prevent usage and assist with cessation in this vulnerable population. Physician education and screening uptake should also be enhanced. Stricter public health policy and protection measures are also needed on a global scale to limit e-cigarette exposure in minors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".