Secondhand Smoke Exposure in Public Places in Guatemala: Comparison with other Latin American Countries
Bibliographic record
Abstract
OBJECTIVE: To measure secondhand smoke levels in workplaces in Guatemala and to compare exposure to levels in other Latin American cities. METHODS: Exposure was estimated by passive sampling of vapor phase nicotine using a filter badge. Filters were placed in 1 hospital, 1 school, 2 universities, 1 government building, the airport, and 10 restaurants/bars. In total, 103 filters were deployed (plus 7 duplicates and 10 blanks). Nicotine (microg/m(3)) was measured by gas chromatography. Medians [interquartile ranges (IQR)] of nicotine concentrations were reported and compared with other Latin American cities. A survey about attitudes for smoke-free workplaces was distributed among employees. RESULTS: Nicotine was detected in most (68%) locations surveyed (including workplaces where smoking is banned). The highest levels were found in bars [median, 4.58 microg/m(3) (IQR, 1.71-6.44)] and restaurants [median, 0.56 microg/m(3) (IQR, 0.46-0.71)]. Nicotine concentrations in bars and restaurants were 710 and 114 times higher, respectively, compared with hospital concentrations after adjustment for smoking ban signs, type of ventilation, and volume of the area. Support for smoke-free environments was high, except in bar/restaurant and airport workers. Airborne nicotine levels in Guatemala were similar to those found in other Latin American cities. CONCLUSION: In Guatemala, exposure to secondhand smoke is highly prevalent. Workers in bars and restaurants are disproportionately exposed to secondhand smoke compared with other workers. There is an urgent need for complete smoke-free legislation and for educating workers about the benefits of smoke-free workplaces.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".