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Trends and predictors of unfilled emergency medicine residency positions: A comparative analysis of the 2023 and 2024 Match cycles.
Preiksaitis, Carl; Abubshait, Layla; Bowers, Kaitlin; Landry, Adaira; Lewis, Kristin; Little, Andrew G; Nash, Christopher J; Gottlieb, Michael.
Afiliação
  • Preiksaitis C; Department of Emergency Medicine Stanford School of Medicine Stanford California USA.
  • Abubshait L; Department of Emergency Medicine Jefferson Einstein Montgomery Hospital East Norrington Pennsylvania USA.
  • Bowers K; Department of Emergency Medicine Campbell University School of Osteopathic Medicine Lillington North Carolina USA.
  • Landry A; Department of Emergency Medicine Brigham and Women's Hospital Boston Massachusetts USA.
  • Lewis K; Department of Emergency Medicine University of California, Irvine Orange California USA.
  • Little AG; Department of Emergency Medicine AdventHealth East Orlando Orlando Florida USA.
  • Nash CJ; Department of Emergency Medicine Duke University Hospital Durham North Carolina USA.
  • Gottlieb M; Department of Emergency Medicine Rush University Medical Center Chicago Illinois USA.
AEM Educ Train ; 8(4): e11013, 2024 Aug.
Article em En | MEDLINE | ID: mdl-39139517
ABSTRACT

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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: AEM Educ Train Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: AEM Educ Train Ano de publicação: 2024 Tipo de documento: Article