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 (LMIC) 的可预防疾病导致数百万儿童死亡 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 EBG 然而，医护人员 (HCW) 之间的差异很大，并导致死亡率增加。移动的 技术已经改变了公共卫生的许多方面，但尚未充分用于医护人员培训。 自适应学习使用电子算法向学习者提供个性化内容，促进 提高资源丰富环境中的学习效率，并可以加强中低收入国家的移动培训平台。 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，我们设计了一个 基于现有 EBG 的自适应电子学习课程，用于管理中低收入国家的儿科疾病。 我们假设适应性电子学习课程将增加医护人员的 EBG 知识 儿童严重疾病的护理以及学习者认知的知识将为创作和学习提供信息 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。知识获取将使用以下方式来衡量 比较课前和课后知识评估的标准平均效应大小。半结构化和 将通过对定量参与者的随机样本进行小组访谈来确定医护人员的习惯和 有关电子学习干预的看法。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。