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All Journal Indonesian Journal of Health Research and Development Journal of Applied Artificial Intelligence in Education
YEM, Sokha
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Computer Science & IT Education Health Professions Medicine & Pharmacology Nursing Public Health

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A Coverage of Maternal Health Checks Before Discharge in Cambodia: A Population-Based Analysis of the 2021-22 Demographic and Health Survey: Evidence from the 2021–22 Cambodia Demographic and Health Survey YEM, Sokha
Indonesian Journal of Health Research and Development Vol. 4 No. 1 (2026): Indonesian Journal of Health Research and Development
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijhrd.v4i1.599

Abstract

Abstract Background: The immediate postpartum period represents a critical window for preventing maternal morbidity and mortality. In Cambodia, facility delivery rates reached 93.9% by 2021-22, yet whether essential quality-of-care processes such as maternal health assessment before discharge are consistently implemented remains uncertain. This study aimed to estimate national coverage of maternal health checks before discharge and quantify missed opportunities among facility deliveries. Methods: We conducted a cross-sectional secondary analysis of the 2021-22 Cambodia Demographic and Health Survey (CDHS), including women aged 15-49 with a most recent live birth in the preceding 24 months (n=3,348 unweighted). The primary outcome was whether maternal health was checked before discharge. Weighted prevalence estimates with 95% confidence intervals (CI) were calculated overall and stratified by sociodemographic and delivery characteristics. Multivariable logistic regression identified factors associated with missed checks among facility deliveries. Results: Nationally, 91.0% of women (95% CI: 89.6-92.2) reported a maternal health check before discharge. Among the 93.9% who delivered in facilities, 91.0% received a check, leaving 9.0% as missed opportunities. Substantial geographic disparities were observed, with coverage ranging from 82.4% in some regions to 96.8% in others. Women with no education (adjusted OR=2.89, 95% CI: 1.45-5.76), rural residence (aOR=1.67, 95% CI: 1.12-2.49), and those in the poorest wealth quintile (aOR=2.34, 95% CI: 1.34-4.09) had significantly higher odds of missed checks. Primiparous women and those with delivery complications also experienced lower coverage. Conclusions: Despite near-universal facility delivery, nearly one in eleven women were discharged without documented maternal health assessment, with pronounced inequities by socioeconomic status, education, and geography. These findings reveal critical gaps in postpartum quality of care that disproportionately affect vulnerable populations. Strengthening standardized discharge protocols, enhancing staff accountability through integrated checklists and supervision, and prioritizing equity in service delivery are essential to ensure consistent, high-quality postpartum care for all women. Keywords: Cambodia; Demographic and Health Survey; postpartum care; quality of care; health equity; maternal health; discharge assessment
AI-Powered Tools to Enhance Critical Thinking and Clinical Reasoning in Nursing Education: A Scoping Review with Implications for Low- and Middle-Income Countries YEM, Sokha; YIM, Sovannra; KEM, Sokunthea; TUN, Sreypeov; Lida, Vann
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.396

Abstract

Artificial Intelligence (AI) is increasingly integrated into educational systems worldwide, offering innovative approaches to improve learning outcomes in health professions education. In nursing, AI-powered tools such as ChatGPT, intelligent tutoring systems (ITS), virtual patient simulation platforms, and automated assessment systems have shown potential to strengthen critical thinking (CT) and clinical reasoning (CR), which are essential competencies for safe and evidence-based practice. However, their scope, effectiveness, and applicability remain underexplored, particularly in low- and middle-income countries (LMICs), where limited digital infrastructure, faculty capacity gaps, and resource constraints hinder implementation. This scoping review aimed to map existing evidence on AI-powered tools used in nursing and health professions education to enhance CT and CR, identify implementation gaps and barriers, and derive context-specific implications for LMICs, with particular attention to Cambodia. Following the Arksey and O’Malley framework, refined by Levac et al. and Peters et al., and guided by PRISMA-ScR, a systematic search was conducted in PubMed, Scopus, and CINAHL for peer-reviewed publications from January 2015 to October 2024. Forty-two studies from 15 countries were included. Four categories of AI tools were identified: conversational agents (n = 14), intelligent tutoring systems (n = 11), virtual patient simulations (n = 10), and automated assessment systems (n = 7). Most studies reported positive outcomes, with seven of eight RCTs showing significant CT improvement and virtual simulations consistently enhancing CR. Nevertheless, infrastructure limitations, faculty unpreparedness, ethical concerns, and licensing costs remain major barriers. Sustainable AI integration in LMIC nursing education requires context-sensitive infrastructure, capacity-building, governance, and stronger longitudinal research.
Co-Authors KEM, Sokunthea Lida, Vann TUN, Sreypeov YIM, Sovannra