Rural trauma team development training amongst medical trainees and traffic law enforcement professionals in a low-income country: a protocol for a prospective multicenter interrupted time series

Author:

Lule Herman12,Mugerwa Michael2,SSebuufu Robinson3,Kyamanywa Patrick4,Posti Jussi P.5,Wilson Michael L.6

Affiliation:

1. Department of Surgery, Kiryandongo Regional Referral Hospital, Kigumba, Uganda

2. Department of Clinical Neurosciences, Injury Epidemiology and Prevention (IEP) Research Group, Turku Brain Injury Centre

3. Department of Surgery, Mengo Hospital, Kampala, Uganda

4. Mother Kevin Postgraduate Medical School, Uganda Martyr’s University, Nkozi, Uganda

5. Department of Neurosurgery and Turku Brain Injury Centre, Neurocentre, Turku University Hospital and University of Turku, Turku, Finland

6. Heidelberg Institute of Global Health (HIGH), University Hospital and University of Heidelberg, Heidelberg, Germany

Abstract

Background: Road traffic injuries and their resulting mortality disproportionately affect rural communities in low-middle-income countries (LMICs) due to limited human and infrastructural resources for postcrash care. Evidence from high-income countries show that trauma team development training could improve the efficiency, care, and outcome of injuries. A paucity of studies have evaluated the feasibility and applicability of this concept in resource constrained settings. The aim of this study protocol is to establish the feasibility of rural trauma team development and training in a cohort of medical trainees and traffic law enforcement professionals in Uganda. Methods: Muticenter interrupted time series of prospective interventional trainings, using the rural trauma team development course (RTTDC) model of the American College of Surgeons. A team of surgeon consultants will execute the training. A prospective cohort of participants will complete a before and after training validated trauma related multiple choice questionnaire during September 2019-November 2023. The difference in mean prepost training percentage multiple choice questionnaire scores will be compared using ANOVA-test at 95% CI. Time series regression models will be used to test for autocorrelations in performance. Acceptability and relevance of the training will be assessed using 3 and 5-point-Likert scales. All analyses will be performed using Stata 15.0. Ethical approval was obtained from Research and Ethics Committee of Mbarara University of Science and Technology (Ref: MUREC 1/7, 05/05-19) and Uganda National Council for Science and Technology (Ref: SS 5082). Retrospective registration was accomplished with Research Registry (UIN: researchregistry9490).

Publisher

Ovid Technologies (Wolters Kluwer Health)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3