The Health Belief Model in predicting healthcare workers' intention for influenza vaccine uptake in Jordan

BackgroundTheories of health behavior change are being inadequately adopted to understand the reasons behind low influenza vaccination rates among healthcare workers (HCWs). The Theory of Planned Behavior (TPB) is being used to predict intention-behavior relationship while the Health Belief Model (HBM) is being employed to predict actual behavior. The purpose of this study was to test a conceptual model based on the HBM’s constructs to predict Jordanian HCWs’ intentions for influenza vaccine uptake as an alternative to the TPB.MethodA cross-sectional questionnaire-based study was conducted in 2016 in a tertiary teaching hospital in Amman-Jordan including a convenience sample of 477 HCWs with direct patient contact. The study instrument was tested for validity and reliability. A conceptual regression model was proposed incorporating the constructs of the primary HBM with some modifications in the threat construct as well as an additional variable about explicit past vaccination behavior (in the past year and/or any previous history of influenza vaccine uptake).ResultsAlmost all the constructs of the HBM demonstrated significant differences between participants intending and those who did not intend to vaccinate against influenza. After adjusting for the confounding variables in the final conceptual regression model, past vaccination behavior (OR= 4.50, 95%Confidence Interval 3.38–6.00, P< 0.0005) and the perceived benefit scale (OR= 1.19, 95% Confidence Interval 1.11–1.28, P< 0.0005) were the only significant predictors of intentions to vaccinate against influenza in the next season.ConclusionTaking into consideration the altruistic beliefs of HCWs and their explicit past vaccination history augments the utility of the original HBM tool in predicting HCWs’ intentions to vaccinate against influenza in a way that is consistent with the predictive ability of the Theory of Planned Behavior.     

Patient-Centered Framework for Rehabilitation Research in Outpatient Settings

Conducting high-quality clinical research is dependent on merging scientific rigor with the clinical environment. This is often a complex endeavor that may include numerous barriers and competing interests. Overcoming these challenges and successfully integrating clinical research programs into clinical practice settings serving rehabilitation outpatients is beneficial from both a logistical perspective (eg, supports efficient and successful research procedures) and the establishment of a truly patient-centered research approach. Leveraging our experience with navigating this research-clinical care relationship, this article (1) proposes the Patient-Centered Framework for Rehabilitation Research, a model for integrating patient-centered research in an outpatient clinical setting that incorporates a collaborative, team-based model encompassing patient-centered values, as well as strategies for recruitment and retention, with a focus on populations living with disabilities or chronic diseases; (2) describes application of this framework in a comprehensive specialty multiple sclerosis center with both general strategies and specific examples to guide adaptation and implementation in other settings; and (3) discusses the effect of the framework as a model in 1 center, as well as the need for additional investigation and adaptation for other populations. The 5 interconnected principles incorporated in the Framework and which prioritize patient-centeredness include identifying shared values, partnering with the clinical setting, engaging with the population, building relationships with individuals, and designing accessible procedures. The Patient-Centered Framework for Rehabilitation Research is a model presented as an adaptable roadmap to guide researchers in hopes of not only improving individual patients’ experiences but also the quality and relevance of rehabilitation research as a whole. Future investigation is needed to test the Framework in other settings.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

对构建德智体美劳全面发展的新医科人才体系的思考

通过分析"新医科"发展趋势以及目前我国医疗供给侧改革的内在需求与人的全面发展之间的紧密联系,提出立德树人、以智启人、以体育人、以美成人、以劳塑人是"新医科"对医学人才培养的主要途径。在此要求之下,医学高等院校应适应全民健康的目标和社会保健服务的需求,坚持本科教育服务地方经济社会的理念,加强大健康教育与全面素质教育,着力培养符合社会需要的、全面发展的新医科人才。     

消化性溃疡在某部军人中的发病情况及相关因素调查

目的 了解部队消化性溃疡（PU）的发病情况及幽门螺杆菌（Hp）感染情况，探讨军人Pu发生的危险因素。方法 采用多阶段分层整群随机抽样的方法，对南方战区某地6160名军人进行PU症状及危险因素问卷调查，并进行胃镜精查、Hp检测。结果 军人PU相关症状发生率（Sc≥5分）18．60％。标化后的PU患病率12．78％，其中军龄1年、南方籍、装甲部队军人PU患病率高。Hp感染率62．67％。Hp感染（OR4．60）、吸烟（OR3．8）、持续高强度军事训练（OR4．3）、驾驶战斗车辆（OR6．0）、经常精神紧张（OR3．7）等与PU症状发生的关系较大。辛辣饮食、常饮牛奶、是否独生子女等与PU发生无关。结论 军人PU患病率及Hp感染率均较高。军人PU发病情况在不同军龄、兵种、入伍前地域之间存在差别，Hp感染、军事训练、精神因素、某些不良生活习惯等与军人PU的发生密切相关。     

医院检验科设备的维修分析

医院检验科设备是为临床提供诊断依据最基础也是最常用的检验手段之一，本文简要介绍了检验科设备在日常工作中容易发生的故障判别及修复时的要点。     

Undergraduate psychiatry teaching in Ethiopia

Many epidemiological studies have shown the magnitude and seriousness of mental disorders in developing countries. However, mental health care remains unsatisfactory owing to lack of skilled manpower and many other social and medical priorities. General practitioners and other health personnel can significantly help in extending mental health care provided they receive adequate training during their medical curriculum. With this aim, the Department of Psychiatry at Addis Ababa University runs a 6-week full-time course for undergraduate medical students. Its chief objectives, teaching methods, achievements and shortcomings are discussed in the paper.