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 ( R 2 = 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.
Vedat Caner (2025) studied this question.