Data-Driven Insights Into Seafood Hazard Analysis Critical Control Points Training Effectiveness: A Dual-Cohort Contingency Analysis and Predictive Modeling

Abstract

This study evaluated the effectiveness of Hazard Analysis and Critical Control Point (HACCP) training programs for seafood safety by integrating contingency analysis and predictive modeling (CA-PM) across two distinct cohorts (n = 15 and 14) from mixed-scale processing industries in Maine, USA. While HACCP training is globally mandated for seafood safety, existing evaluations are largely descriptive, with critical gaps in predictive models linking demographics to behavioral outcomes, analysis of contextual factors, and multi-cohort designs. This study aimed to integrate CA-PM to resolve these interactions and forecast behavioral outcomes. Surveys collected demographic/professional predictors and 51 outcomes from seafood industry professionals participating in two HACCP training programs. Fisher's exact tests and uncertainty coefficients were used to identify associations, while 10 data-driven algorithms predicted outcomes. Overall, 45% (23/51) of outcomes were successfully modeled according to their validation-based coefficient of determination (R²). Artificial neural network (ANN) models achieved better predictive power for binary outcomes than the other models. Dual-cohort validation revealed consistent trends (e.g., experience boosted confidence; p = 0.020). Participants with > 7 years' experience reported 100% satisfaction and higher confidence. Females showed stronger intent to revise HACCP plans (p = 0.029). Perceived complexity reduced technology adoption intent (p = 0.033). Pre-training gender disparities in HACCP familiarity were eliminated post-training (ANN R² = 100%). HACCP training should be tailored based on experience, gender, and local regulatory context. Future research should validate these findings in larger cohorts and apply integrated analytics to guide targeted improvements in training and resource planning.