未填补急诊医学住院医师职位的趋势和预测因素:对 2023 年和 2024 年匹配周期的比较分析

IF 1.7 Q2 EDUCATION, SCIENTIFIC DISCIPLINES AEM Education and Training Pub Date : 2024-08-11 DOI:10.1002/aet2.11013
Carl Preiksaitis MD, MEd, Layla Abubshait MD, Kaitlin Bowers DO, Adaira Landry MD, MEd, Kristin Lewis MD, Andrew G. Little DO, Christopher J. Nash MD, EdM, Michael Gottlieb MD
{"title":"未填补急诊医学住院医师职位的趋势和预测因素:对 2023 年和 2024 年匹配周期的比较分析","authors":"Carl Preiksaitis MD, MEd,&nbsp;Layla Abubshait MD,&nbsp;Kaitlin Bowers DO,&nbsp;Adaira Landry MD, MEd,&nbsp;Kristin Lewis MD,&nbsp;Andrew G. Little DO,&nbsp;Christopher J. Nash MD, EdM,&nbsp;Michael Gottlieb MD","doi":"10.1002/aet2.11013","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The emergency medicine (EM) landscape has evolved due to the increasing number of programs paired with fewer applicants. This study analyzed the characteristics of EM residency programs associated with unfilled positions during the 2024 Match and compared them with data from the 2023 Match to identify persistent and emerging trends influencing these outcomes.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>In this cross-sectional, observational study, we investigated factors associated with unfilled EM residency positions in the 2024 Match. We used publicly accessible data from the National Resident Matching Program. To identify program-level predictors of unfilled positions, we constructed a Bayesian hierarchical logistic regression model, incorporating data from the 2023 Match season.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>In 2024, 54 out of 281 (19.2%) residency programs remained unfilled. Our Bayesian analysis reaffirmed that smaller program size, geographical location, prior osteopathic accreditation, and corporate ownership continue to be significant factors. Programs with vacancies in the previous year were also more likely to remain unfilled. Thus, several factors identified in 2023 remained associated with this year's Match outcomes, with the impact of previous unfilled positions being particularly pronounced.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>This study identified several factors associated with a greater likelihood of having unfilled EM residency positions, with previous unfilled positions emerging as the most significant predictor. These findings offer critical insights for residency programs and governing bodies, providing a basis for enhancing recruitment strategies, addressing the cyclical nature of unfilled positions, and tackling workforce challenges in EM.</p>\n </section>\n </div>","PeriodicalId":37032,"journal":{"name":"AEM Education and Training","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trends and predictors of unfilled emergency medicine residency positions: A comparative analysis of the 2023 and 2024 Match cycles\",\"authors\":\"Carl Preiksaitis MD, MEd,&nbsp;Layla Abubshait MD,&nbsp;Kaitlin Bowers DO,&nbsp;Adaira Landry MD, MEd,&nbsp;Kristin Lewis MD,&nbsp;Andrew G. Little DO,&nbsp;Christopher J. Nash MD, EdM,&nbsp;Michael Gottlieb MD\",\"doi\":\"10.1002/aet2.11013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The emergency medicine (EM) landscape has evolved due to the increasing number of programs paired with fewer applicants. This study analyzed the characteristics of EM residency programs associated with unfilled positions during the 2024 Match and compared them with data from the 2023 Match to identify persistent and emerging trends influencing these outcomes.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>In this cross-sectional, observational study, we investigated factors associated with unfilled EM residency positions in the 2024 Match. We used publicly accessible data from the National Resident Matching Program. To identify program-level predictors of unfilled positions, we constructed a Bayesian hierarchical logistic regression model, incorporating data from the 2023 Match season.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>In 2024, 54 out of 281 (19.2%) residency programs remained unfilled. Our Bayesian analysis reaffirmed that smaller program size, geographical location, prior osteopathic accreditation, and corporate ownership continue to be significant factors. Programs with vacancies in the previous year were also more likely to remain unfilled. Thus, several factors identified in 2023 remained associated with this year's Match outcomes, with the impact of previous unfilled positions being particularly pronounced.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>This study identified several factors associated with a greater likelihood of having unfilled EM residency positions, with previous unfilled positions emerging as the most significant predictor. These findings offer critical insights for residency programs and governing bodies, providing a basis for enhancing recruitment strategies, addressing the cyclical nature of unfilled positions, and tackling workforce challenges in EM.</p>\\n </section>\\n </div>\",\"PeriodicalId\":37032,\"journal\":{\"name\":\"AEM Education and Training\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AEM Education and Training\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aet2.11013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AEM Education and Training","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aet2.11013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0

摘要

背景 急诊医学(EM)的格局已经发生了变化,原因是越来越多的项目与越来越少的申请者配对。本研究分析了与 2024 年比赛中未填补职位相关的急诊科住院医师培训项目的特点,并将其与 2023 年比赛的数据进行比较,以确定影响这些结果的持续和新兴趋势。 方法 在这项横断面观察性研究中,我们调查了与 2024 年比赛中未填补的急诊科住院医师职位相关的因素。我们使用了国家住院医师配对计划的公开数据。为了确定未填补职位的项目级预测因素,我们建立了一个贝叶斯分层逻辑回归模型,并纳入了 2023 年比赛季的数据。 结果 2024 年,281 个住院医师项目中有 54 个(19.2%)仍未完成。我们的贝叶斯分析再次证实,较小的项目规模、地理位置、之前的骨科认证和公司所有权仍然是重要因素。上一年出现空缺的项目也更有可能继续空缺。因此,2023 年发现的几个因素仍然与今年的 Match 结果有关,其中以前未填补职位的影响尤为明显。 结论 本研究发现了几个与更有可能出现未填补的急诊科住院医师职位相关的因素,其中以前未填补的职位是最重要的预测因素。这些发现为住院医师培训项目和管理机构提供了重要的启示,为加强招聘策略、解决职位空缺的周期性问题以及应对急诊科劳动力挑战提供了依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Trends and predictors of unfilled emergency medicine residency positions: A comparative analysis of the 2023 and 2024 Match cycles

Background

The emergency medicine (EM) landscape has evolved due to the increasing number of programs paired with fewer applicants. This study analyzed the characteristics of EM residency programs associated with unfilled positions during the 2024 Match and compared them with data from the 2023 Match to identify persistent and emerging trends influencing these outcomes.

Methods

In this cross-sectional, observational study, we investigated factors associated with unfilled EM residency positions in the 2024 Match. We used publicly accessible data from the National Resident Matching Program. To identify program-level predictors of unfilled positions, we constructed a Bayesian hierarchical logistic regression model, incorporating data from the 2023 Match season.

Results

In 2024, 54 out of 281 (19.2%) residency programs remained unfilled. Our Bayesian analysis reaffirmed that smaller program size, geographical location, prior osteopathic accreditation, and corporate ownership continue to be significant factors. Programs with vacancies in the previous year were also more likely to remain unfilled. Thus, several factors identified in 2023 remained associated with this year's Match outcomes, with the impact of previous unfilled positions being particularly pronounced.

Conclusions

This study identified several factors associated with a greater likelihood of having unfilled EM residency positions, with previous unfilled positions emerging as the most significant predictor. These findings offer critical insights for residency programs and governing bodies, providing a basis for enhancing recruitment strategies, addressing the cyclical nature of unfilled positions, and tackling workforce challenges in EM.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
AEM Education and Training
AEM Education and Training Nursing-Emergency Nursing
CiteScore
2.60
自引率
22.20%
发文量
89
期刊最新文献
Issue Information Paths to learning: How residents navigate transience in supervisory relationships in the emergency department General emergency physician perceptions of caring for children: A qualitative interview study 5, 4, 3, 2, 1, 0: An evidence-based mnemonic to aid recall and interpretation of heart rate values for pediatric patients presenting for acute care “Ardor and diligence”: Quantifying the faculty effort needed in emergency medicine graduate medical education
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1