Original Article The Effect of Work Practices on Personal Exposure to Glutaraldehyde among Health Care Workers
Bibliographic record
Abstract
Abstract: Glutaraldehyde is a potential sensitizer and has been implicated in the literature as a cause of respiratory irritation and asthma among health care workers. In order to evaluate the effect of work practices and general ventilation system on employees ’ peak exposure to glutaraldehyde, 42 breathing zone personal air samples were taken in five hospitals. In addition, work practices were observed and recorded during the course of sampling and were classified into three categories. Presence of local or general ventilation system, air change per hour, and quantity of glutaraldehyde used were also recorded. Geometric mean concentration of all samples was 0.025 ppm (GSD=3.05). Statistical analysis indicated that work practice was the most important factor affecting the level of exposure to glutaraldehyde. In locations where “poor ” or “unsafe ” work practices were employed, the geometric mean concentrations were much higher (GM=0.05, GSD=2.11 and GM 0.08, GSD=1.52, respectively). The result has indicated higher prevalence of headache and itchy eyes among employees who worked where unsafe work practices were observed. Employing proper work practices can significantly reduce exposure to glutaraldehyde among health care workers. It has been recently proposed that the current occupational exposure limit of 0.2 ppm shall be reduced to either 0.1 or 0.05 ppm in the province of Québec (Canada). In this case, it is likely that concentration levels higher than these levels be experienced in some workplaces. Therefore, it is imperative that employers initiate necessary corrective action immediately.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".