Longitudinal KAP Analysis for Measuring the Robustness of Community Nurses’ Digital Health Adoption
(1) Research Unit, Training and Human Development Center, Al-Diwaniyah Health Directorate, Ministry of Health, Al-Diwaniyah Governorate 00964, Iraq.
(*) Corresponding Author
Abstract
The rapid integration of digital health architectures demands specialized digital health literacy among frontline
clinicians. However, in resource-constrained healthcare environments, professional readiness and technological
acceptance remain limited. This study evaluates a structured training intervention designed to optimize the
Knowledge, Attitude, and Practice (KAP) trajectories of community health nurses, leveraging the causal pathways
of the Technology Acceptance Model (TAM). Using an explanatory sequential mixed-methods design, a cohort of
60 community health nurses underwent a four-session simulator-based training. Metrics were quantified across
multi-temporal checkpoints: Baseline Spre, 1 month Spost1, and 3 months Spost2. Hypothesis testing confirmed
statistically significant and robust competency transitions (p < 0.001). Cognitive Knowledge (K) scores escalated
from 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 elite
effect 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 adoption
efficiency of 93% and a low degradation rate ( = 0.046). Qualitative insights from interviews (n = 15) highlighted
infrastructural constraints ( ) limiting the theoretical performance ceiling.
Keywords
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DOI: https://doi.org/10.26714/jichi.v7i2.21037
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Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
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Department of Informatics, Faculty of Computer Science and Information Technology
Universitas Muhammadiyah Semarang
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