Patterson Fiona, Baron Helen, Carr Victoria, Plint Simon, Lane Pat
Department of Psychology, City University, London, UK.
Med Educ. 2009 Jan;43(1):50-7. doi: 10.1111/j.1365-2923.2008.03238.x.
This study aimed to evaluate the effectiveness and efficiency of three short-listing methodologies for use in selecting trainees into postgraduate training in general practice in the UK.
This was an exploratory study designed to compare three short-listing methodologies. Two methodologies - a clinical problem-solving test (CPST) and structured application form questions (AFQs) - were already in use for selection purposes. The third, a new situational judgement test (SJT), was evaluated alongside the live selection process. An evaluation was conducted on a sample of 463 applicants for training posts in UK general practice. Applicants completed all three assessments and attended a selection centre that used work-related simulations at final stage selection. Applicant scores on each short-listing methodology were compared with scores at the selection centre.
Results indicate the structured AFQs, CPST and SJT were all valid short-listing methodologies. The SJT was the most effective independent predictor. Both the structured AFQs and the SJT add incremental validity over the use of the CPST alone. Results show that optimum validity and efficiency is achieved using a combination of the CPST and SJT.
A combination of the CPST and SJT represents the most effective and efficient battery of instruments as, unlike AFQs, these tests are machine-marked. Importantly, this is the first study to evaluate a machine-marked SJT to assess non-clinical domains for postgraduate selection. Future research should explore links with work-based assessment once trainees are in post to address long-term predictive validity.
本研究旨在评估三种筛选方法在选拔英国全科医学研究生培训学员时的有效性和效率。
这是一项探索性研究,旨在比较三种筛选方法。两种方法——临床问题解决测试(CPST)和结构化申请表问题(AFQ)——已用于选拔目的。第三种是新的情境判断测试(SJT),与实际选拔过程一起进行评估。对463名申请英国全科医学培训岗位的申请人进行了评估。申请人完成了所有三项评估,并参加了在最终选拔阶段使用与工作相关模拟的选拔中心。将每种筛选方法的申请人分数与选拔中心的分数进行比较。
结果表明,结构化AFQ、CPST和SJT都是有效的筛选方法。SJT是最有效的独立预测指标。结构化AFQ和SJT单独使用CPST时都增加了增量效度。结果表明,结合使用CPST和SJT可实现最佳效度和效率。
CPST和SJT的组合代表了最有效和高效的一套工具,因为与AFQ不同,这些测试是机器评分的。重要的是,这是第一项评估机器评分SJT以评估研究生选拔非临床领域的研究。未来的研究应该在学员入职后探索与基于工作的评估的联系,以解决长期预测效度问题。