A Data-driven Approach to Selecting Pulmonary and Critical Care Fellows for Interviews.

IF 1.7 Q3 CRITICAL CARE MEDICINE
ATS scholar Pub Date : 2025-03-01 Epub Date: 2025-01-22 DOI:10.34197/ats-scholar.2024-0007IN
Jordan A Kempker, Ashish J Mehta, J Shirine Allam
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引用次数: 0

Abstract

Background: Training programs around the country receive many applications every year with a limited time window to send out invitations for interviews. This poses major barriers to conducting holistic application reviews. Objective: To create and implement a data-driven selection process that promotes holistic reviews within a tight timeline to select applicants for invitation to interview. Methods: In 2022, we conducted a survey of clinical faculty and fellows to ascertain the experiences, attributes, metrics, and characteristics deemed important for success in our training environment. We formed a selection committee and used the survey results to construct an automated screening tool and a faculty-completed application review form with resultant data summarized to aid in data-supported selection decisions. Results: Among the 60 survey respondents, 42 (71%) were faculty members and 17 (29%) were current fellows. The six most important items for trainee success fell under the domain of leadership attributes. Survey results were used to create a weighted screening score that was used for initial triaging of applications and a weighted faculty-reviewed application score standardized to each faculty reviewer and used to select applicants for interviews. These sequential scores allowed a holistic review of 306 applications by 20 faculty in a time-sensitive manner. Conclusion: Survey methods can be used to generate weighted and standardized application assessment tools that allow data-supported fellow selection decisions and facilitate holistic application reviews.

数据驱动的方法选择肺部和重症监护研究员的访谈。
背景:全国各地的培训项目每年都会收到许多申请,而发出面试邀请的时间有限。这对进行全面的应用程序审查构成了主要障碍。目标:创建并实施一个数据驱动的选择过程,促进在紧迫的时间内进行全面审查,以选择邀请面试的申请人。方法:在2022年,我们对临床教师和研究员进行了一项调查,以确定在我们的培训环境中成功的重要经验、属性、指标和特征。我们成立了一个选择委员会,并使用调查结果构建了一个自动筛选工具和一个由教师完成的申请审查表格,并汇总了结果数据,以帮助数据支持的选择决策。结果:在60名受访者中,42名(71%)是教职员工,17名(29%)是现任研究员。学员成功的六个最重要的项目属于领导属性的范畴。调查结果用于创建加权筛选分数,该分数用于应用程序的初始分类,加权教师审查的应用程序分数标准化到每个教师审查人员,并用于选择申请人进行面试。这些连续的分数允许对20名教员的306份申请进行全面审查,时间紧迫。结论:调查方法可用于生成加权和标准化的应用评估工具,允许数据支持的同伴选择决策,并促进整体应用审查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
0.00%
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0
审稿时长
11 weeks
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