Longitudinal KAP Analysis for Measuring the Robustness of Community Nurses’ Digital Health Adoption
TL;DR
A structured training intervention designed to optimize the Knowledge, Attitude, and Practice trajectories of community health nurses, leveraging the causal pathway of the Technology Acceptance Model (TAM), revealing an exceptional overall technology adoptionefficiency and a low degradation rate.
Abstract
The rapid integration of digital health architectures demands specialized digital health literacy among frontlineclinicians. However, in resource-constrained healthcare environments, professional readiness and technologicalacceptance remain limited. This study evaluates a structured training intervention designed to optimize theKnowledge, Attitude, and Practice (KAP) trajectories of community health nurses, leveraging the causal pathwaysof the Technology Acceptance Model (TAM). Using an explanatory sequential mixed-methods design, a cohort of60 community health nurses underwent a four-session simulator-based training. Metrics were quantified acrossmulti-temporal checkpoints: Baseline Spre, 1 month Spost1, and 3 months Spost2. Hypothesis testing confirmedstatistically significant and robust competency transitions (p < 0.001). Cognitive Knowledge (K) scores escalatedfrom a baseline of 12.4 ± 3.1 to 24.2 ± 1.8 at 1 month, and stabilized at 23.5 ± 2.1 at 3 months, reflecting an eliteeffect size (Cohen’s d = 2.61). Behavioral Attitude (A) scores advanced from 3.2 ± 0.8 to 4.6 ± 0.4 (p < 0.001),while Clinical Practice (P) shifted from 2.1 ± 1.2 to 4.1 ± 0.7, revealing an exceptional overall technology adoptionefficiency of 93% and a low degradation rate ( = 0.046). Qualitative insights from interviews (n = 15) highlightedinfrastructural constraints ( ) limiting the theoretical performance ceiling.