• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Medical Education >Ambulatory teaching: Do approaches to learning predict the site and preceptor characteristics valued by clerks and residents in the ambulatory setting?
【24h】

Ambulatory teaching: Do approaches to learning predict the site and preceptor characteristics valued by clerks and residents in the ambulatory setting?

机译：门诊教学：学习方法是否可以预测门诊环境中的职员和居民所重视的场所和感受器特征？

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background In a study to determine the site and preceptor characteristics most valued by clerks and residents in the ambulatory setting we wished to confirm whether these would support effective learning. The deep approach to learning is thought to be more effective for learning than surface approaches. In this study we determined how the approaches to learning of clerks and residents predicted the valued site and preceptor characteristics in the ambulatory setting. Methods Postal survey of all medical residents and clerks in training in Ontario determining the site and preceptor characteristics most valued in the ambulatory setting. Participants also completed the Workplace Learning questionnaire that includes 3 approaches to learning scales and 3 workplace climate scales. Multiple regression analysis was used to predict the preferred site and preceptor characteristics as the dependent variables by the average scores of the approaches to learning and perception of workplace climate scales as the independent variables. Results There were 1642 respondents, yielding a 47.3% response rate. Factor analysis revealed 7 preceptor characteristics and 6 site characteristics valued in the ambulatory setting. The Deep approach to learning scale predicted all of the learners' preferred preceptor characteristics (β = 0.076 to β = 0.234, p Direction was more strongly associated with the Surface Rational approach (β = .252, p Surface Disorganized approach to learning (β = .154, p Deep approach. The Deep approach to learning scale predicted valued site characteristics of Office Management, Patient Logistics, Objectives and Preceptor Interaction (p Surface Rational approach to learning predicted valuing Learning Resources and Clinic Set-up (β = .09, p = .001; β = .197, p Surface Disorganized approach to learning weakly negatively predicted Patient Logistics (β = -.082, p = .003) and positively the Learning Resources (β = .088, p = .003). Climate factors were not strongly predictive for any studied characteristics. Role Modeling and Patient Logistics were predicted by Supportive Receptive climate (β = .135, p Conclusion Most site and preceptor characteristics valued by clerks and residents were predicted by their Deep approach to learning scores. Some characteristics reflecting the need for good organization and clear direction are predicted by learners' scores on less effective approaches to learning.
机译：背景技术在一项研究中，确定门诊环境中的职员和居民最看重的场所和感受器特征，我们希望确认这些信息是否会支持有效的学习。深度学习方法被认为比表面学习方法更有效。在这项研究中，我们确定了学习文员和居民的方法如何预测非卧床环境中有价值的位置和受体特征。方法对安大略省所有接受培训的医疗居民和文员进行邮政调查，以确定在非卧床环境中最有价值的部位和感受器特征。参与者还完成了“工作场所学习”问卷，其中包括3种学习量表的方法和3种工作场所气候量表。多元回归分析用于通过学习和感知工作场所气候尺度的方法的平均得分作为自变量来预测首选站点和受体特征作为因变量。结果共有1642名受访者，回应率为47.3％。因子分析揭示了在动态环境中有价值的7个受体特征和6个位点特征。深度学习方法可预测学习者的所有首选感知器特征（β= 0.076至β= 0.234，p方向与表面有理方法（β= .252，p表面无组织学习法（β= .154，p深度法。深度法学习规模可预测办公室管理，患者后勤，目标和受体相互作用的有价值的场所特征（p表面理性方法可预测评估学习资源和诊所设置的价值（β= .09， p = .001;β= .197，p表面无序学习方法对弱预测的患者后勤（β= -.082，p = .003）和积极学习资源（β= .088，p = .003）的学习较弱。气候因素对所研究的特征没有强烈的预测作用，角色模型和患者后勤工作是通过支持性接受气候来预测的（β= .135，p结论大多数文员和居民所重视的场所和感受器特征都是由他们对学习成绩的深入研究方法决定。学习者在不太有效的学习方法上的得分会预测出一些特征，这些特征反映了对良好组织和清晰方向的需求。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号