Electronic Cigarettes: Exposure to secondhand vapors at a long-term healthcare company
Bibliographic record
Abstract
Introduction: Healthcare workers in long term care settings have limited control over their occupational secondhand exposure to electronic cigarettes and other tobacco products. Methods: The study aimed to identify the perceived frequency of exposure to exhaled electronic cigarette vapor on healthcare workers within two sites of a long-term healthcare company. An online survey was completed by 149 (out of approximately 500) employees that asked about electronic cigarette personal usage, concerns for exposure, exposure times, and demographic data. Results: Twelve percent of all survey respondents expressed concerns related to second-hand exposure. Of those exposed, employee estimated exposure time was 2.1 minutes per shift for electronic cigarettes compared to 12.1 minutes per shift for cigarettes/cigars/pipes. Conclusions: Overall self-reported secondhand exposure to electronic cigarettes and cigarettes/cigars/pipes was low. To determine a definitive exposure level, quantitative sampling can be done related to chemical exposure via passive inhalation of the smoke and vapor cloud for cigarettes and electronic cigarettes, respectively. Education can be provided to healthcare workers and residents in long-term care facilities regarding risk of exposure to secondhand smoke to alleviate employees concerns with exposure.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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".