考试预测和一般实践注册官能力评估网格 (GPR-CAG)
Katie Fisher1,2, Alison Fielding1,2, Anna Ralston1,2
1School of Medicine and Public Health, University of Newcastle, University Drive, Callaghan, NSW, Australia.
概括
外部临床教学访问 (ECTV) 的表现,通过GP注册师能力评估网格 (GPR-CAG) 来衡量,预测澳大利亚皇家全科医生学院 (RACGP) 奖学金考试的成功. 通过GPR-CAG分数,可以提前识别有风险的注册商.
科学领域:
- 医学教育 医学教育
- 一般实践培训一般实践培训
- 评估和评价的评估和评估.
背景情况:
- 识别在一般实践 (GP) 培训中表现不佳的早期预测因素至关重要.
- 总结性检查的表现是GP注册人员的一个关键结果.
- 外部临床教学访问 (ECTV) 为早期评估提供了一个潜在的途径.
研究的目的:
- 为了确定使用GP注册人员能力评估网格 (GPR-CAG) 评估的外部临床教学访问 (ECTV) 绩效,预测澳大利亚皇家全科医生学院 (RACGP) 奖学金考试结果.
- 在总结性GP考试上评估特定GPR-CAG因素的预测有效性.
主要方法:
- 新南威尔士州/ACT (2014-2018) 的全科医生注册人员的回顾性队列研究.
- 数据包括第一个培训学期的GPR-CAG分数 (GPT1) 和奖学金考试结果 (AKT,KFP,OSCE).
- 用单变量和多变量回归分析来评估关联.
主要成果:
- 所有四个GPR-CAG因子都显示出与大多数Fellowship考试结果的显著不变关联 (p < 0.01).
- 身体检查技能与所有四个考试结果都显示出显著的多变量关联 (p < 0.05).
- 在多变量分析中,每个GPR-CAG因子与至少一个考试结果显著相关.
结论:
- 根据GPR-CAG分数衡量的ECTV表现,是RAGP奖学金考试表现的重要预测指标.
- GPR-CAG评分在识别处于表现不佳风险的注册商方面具有务实的实用性,从而使早期干预成为可能.
- GPR-CAG评分的预测能力超出了其他已知的预测因素.
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