Multivariate analyses of risk factors associated with laboratory exposure incidents
Bibliographic record
Abstract
Background: Laboratories involved in the study of pathogenic biological agents pose an inherent risk of exposure to the laboratory workforce and the community. Laboratory biosafety and biosecurity activities are fundamental in minimizing the likelihood of unintentional exposure incidents. The objective of this study is to describe the factors that are associated with the occurrence of exposure incidents in a laboratory setting through a predictive model. Methods: The Laboratory Incident Notification Canada is a nationally mandated surveillance system that gathers real-time data from submitted reports of laboratory incidents involving human pathogens and toxins. Data on laboratory exposure incidents were extracted from the system between 2016 and 2020. The occurrence of exposure incidents per month was modelled using a Poisson regression with several potential risk factors, including seasonality, sector, occurrence type, root causes, role and education of people exposed and years of laboratory experience. A stepwise selection method was used to develop a parsimonious model with consideration of the significant risk factors identified in the literature. Results: =0.0010). Conclusion: Laboratory biosafety and biosecurity activities should target these risk factors to reduce the occurrence of exposure incidents. Qualitative studies are needed to provide better reasoning for the association of these risk factors with the occurrence of exposure incidents.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".