Mixed-Methods Evaluation of Mobile Health Adaptive Learning Training for Pediatric Healthcare Workers in Tanzania

坦桑尼亚儿科医护人员移动健康适应性学习培训的混合方法评估

基本信息

批准号：
10863717
负责人：
Zachary Haines Smith
金额：
$ 0.25万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-20 至 2023-07-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10863717
关键词：
Address Adherence Affect Algorithms Award COVID-19 Caring Cessation of life Child Child health care Childhood Clinical Research Critically ill children Data Development Devices Double-Blind Method E-learning Education Education Gap Educational Curriculum Electronics Ensure Equity Evaluation Focus Groups Foundations Funding Future Goals Group Interviews Habits Health Personnel Health system Healthcare Human Resources Individual International Intervention Interview Investigation Knowledge Knowledge acquisition Learning Measures Medical center Methods Participant Patients Pediatric cohort Perception Persons Public Health Qualitative Methods Quality of Care Random Allocation Randomized Randomized, Controlled Trials Research Research Design Research Personnel Resource-limited setting Resources Sampling Standardization Structure Tanzania Technology Training Training Programs World Health Organization acute care adaptive learning arm burden of illness career development cohort comparative cost design efficacy evaluation evidence based guidelines future implementation implementation cost implementation strategy improved improved outcome innovation international partnership learning strategy low and middle-income countries mHealth mobile computing mortality new technology pediatric patients post intervention prospective tool treatment arm

项目摘要

PROJECT ABSTRACT Preventable illness in low- and middle-income countries (LMICs) contributes to millions of pediatric deaths each year. Evidence-based guidelines (EBG) created by the World Health Organization are shown to improve outcomes for pediatric patients in LMICs and reduce this amenable burden of disease. Utilization of existing EBG is highly variable among healthcare workers (HCWs), however, and contributes to increased mortality. Mobile technology has transformed many aspects of public health, but is yet to be fully leveraged for HCW training. Adaptive learning, which uses electronic algorithms to deliver individualized content to learners, promotes increased learning efficiency in high-resource settings and could strengthen mobile training platforms in LMICs. To evaluate the efficacy of these tools in addressing the existing educational gap in LMICs, we designed an adaptive electronic learning curriculum based on existing EBG for the management pediatric illness in LMICs. We hypothesize that an adaptive electronic learning curriculum will increase HCW knowledge of EBG for the care of serious illness in children and that knowledge of learner perceptions will inform the creation and implementation of future electronic learning interventions. We will conduct a mixed-methods study among a cohort of pediatric HCWs in Mwanza, Tanzania. Quantitative study design will be prospective randomized parallel-group double- blinded with an allocation ratio of 1:1. All participants will complete an electronic learning curriculum on priority content areas as defined by local stakeholders. Participants in the intervention arm will receive an adaptive electronic learning curriculum, and controls will receive a non-adaptive electronic learning curriculum. Knowledge acquisition will be measured using standard mean effect size comparing pre- and post-curriculum knowledge assessments. Semi-structured and group interviews with a random sample of quantitative participants will be used to determine HCW habits and perceptions relating to electronic learning interventions. Successful completion of the proposed research will serve as the foundation for the development of innovative solutions and low-cost implementation strategies aimed at improving the care of seriously ill children worldwide.

项目摘要低收入和中等收入国家 (LMIC) 的可预防疾病导致数百万儿童死亡每年。世界卫生组织制定的循证指南（EBG）被证明可以改善中低收入国家儿科患者的治疗结果，并减少这种可承受的疾病负担。利用现有的然而，EBG 在医护人员 (HCW) 中差异很大，并且会导致死亡率增加。移动技术已经改变了公共卫生的许多方面，但尚未被医护人员充分利用训练。自适应学习，使用电子算法向学习者提供个性化内容，促进资源丰富环境中学习效率的提高，并可以加强移动培训平台在中低收入国家。为了评估这些工具在解决中低收入国家现有教育差距方面的有效性，我们基于现有的 EBG 为管理儿科设计了自适应电子学习课程中低收入国家的疾病。我们假设适应性电子学习课程将增加医护人员的知识 EBG 用于治疗儿童严重疾病的知识，并且了解学习者的看法将告知创建和实施未来的电子学习干预措施。我们将对坦桑尼亚姆万扎的一组儿科医护人员进行混合方法研究。定量研究设计将是前瞻性随机平行组双盲，分配比例为1:1。全部参与者将完成当地规定的优先内容领域的电子学习课程利益相关者。干预组的参与者将接受适应性电子学习课程，并且控件将接受非自适应电子学习课程。知识获取将被衡量使用标准平均效应大小来比较课前和课后知识评估。半结构化并对定量参与者的随机样本进行小组访谈，以确定医护人员的习惯以及与电子学习干预相关的看法。成功完成拟议的研究将作为开发创新解决方案和低成本实施策略的基础旨在改善全世界重病儿童的护理。