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

• 批准号: 10538413
• 负责人: Zachary Haines Smith
• 金额: $ 7.91万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-07-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10538413
• 关键词: Address; Adherence; Affect; Algorithms; Area; Award; COVID-19; Caring; Cessation of life; Child; Childhood; Clinical Research; Critically ill children; Data; Development; Devices; Double-Blind Method; E-learning; Education; Educational Curriculum; Ensure; 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; Logistics; Measures; Medical center; Methods; Participant; Patients; Pediatric cohort; Perception; Persons; Public Health; Qualitative Methods; Quality of Care; 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; base; 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

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的可预防疾病分别导致数百万儿童死亡 年。由世界卫生组织创建的循证指南(EBG)显示出改进 这将有助于改善LMICs中儿童患者的预后，并减少这一可承担的疾病负担。利用现有的EBG 然而，在医护人员(HCW)中有很大的变数，并导致死亡率增加。莫比尔县 技术已经改变了公共卫生的许多方面，但尚未充分利用技术进行卫生保健培训。 自适应学习使用电子算法向学习者提供个性化的内容，促进 提高高资源环境下的学习效率，并可加强小岛屿发展中国家的流动培训平台。 为了评估这些工具在解决LMIC现有教育差距方面的有效性，我们设计了一个 基于现有EBG的适应性电子学习课程，用于管理LMICs中的儿科疾病。 我们假设，自适应的电子学习课程将增加HCW对EBG的了解 对儿童严重疾病护理和对学习者感知的了解将为创建和 实施未来的电子学习干预措施。 我们将在坦桑尼亚姆万扎的一群儿科医务工作者中进行一项混合方法研究。量化 研究设计为前瞻性、随机、平行、双盲，分配比例为1：1。 学员将完成电子学习课程，内容涉及当地政府定义的优先内容领域 利益相关者。干预组的参与者将接受适应性电子学习课程，以及 Controls将接受非适应性电子学习课程。知识获取将使用以下方法进行衡量 比较课程前后知识评估的标准平均效果大小。半结构化和 对量化参与者的随机抽样的小组访谈将被用来确定卫生保健服务习惯和 与电子学习干预相关的认知。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战略的基础 旨在改善世界各地重病儿童的护理。