Year: 2025
Type: article
Study design: Observational Study
Abstract: Introduction Laboratory safety behavior is crucial for minimizing risks in high-hazard clinical settings, yet behavioral non-compliance persists as a leading cause of laboratory accidents despite established protocols. Methods This study evaluated safety behavior among 92 personnel employed in genetic diagnostic laboratories in Istanbul using a validated 34-item safety behavior scale. Principal component analysis (PCA), multiple linear regression, and k-nearest neighbors (k-NN) classification were employed to analyze the data. Results The analysis revealed three underlying behavioral dimensions: personal compliance, proactive behavior, and institutional engagement. Regression analysis indicated that perceived institutional support and the frequency of safety training were significant predictors of overall safety behavior (R2 = 0.47, p < 0.001). Furthermore, the k-NN classifier utilizing PCA-derived components achieved an 88% accuracy rate in distinguishing high and low compliance profiles. Discussion These findings underscore the utility of multivariate behavioral analytics in profiling laboratory safety behavior and highlight the potential of data-informed, classification-based strategies to enhance safety interventions. Adopting behaviorally tailored approaches to training and institutional support may markedly improve compliance and mitigate risk in laboratory environments.
Source: Frontiers in Public Health
Author: Vedat Caner
Institution: Beykent University
Language: English
Subfield: Chemical Health and Safety
Field: Chemical Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4414527904