Background: Despite the critical nature of the residency interview process, few metrics have been shown to adequately predict applicant success in matching to a given program. While evaluating and ranking potential candidates, bias can occur when applicants make commitment statements to a program. Survey data show that pressure to demonstrate commitment leads applicants to express commitment to multiple institutions including telling >1 program that they will rank them #1. The primary purpose of this cross-sectional observational study is to evaluate the frequency of commitment statements from applicants to 5 anesthesiology departments during a single interview season, report how often each statement is associated with a successful match, and identify how frequently candidates incorrectly represented commitments to rank a program #1.

Methods: During the 2014 interview season, 5 participating anesthesiology programs collected written and verbal communications from applicants. Three residency program directors independently reviewed the statements to classify them into 1 of 3 categories; guaranteed commitment, high rank commitment, or strong interest. Each institution provided a deidentified rank list with associated commitment statements, biographical data, whether candidates were ranked-to-match, and if they successfully matched.

Results: Program directors consistently differentiated among strong interest, high rank, and guaranteed commitment statements with κ coefficients of 0.9 (95% CI, 0.8-0.9) or greater between any pair of reviewers. Overall, 35.8% of applicants (226/632) provided a statement demonstrating at least strong interest and 5.4% (34/632) gave guaranteed commitment statements. Guaranteed commitment statements resulted in a 95.7% match rate to that program in comparison to statements of high rank (25.6%), strong interest (14.6%), and those who provided no statement (5.9%). For those providing guaranteed commitment statements, it can be assumed that the 1 candidate (4.3%) who did not match incorrectly represented himself. Variables such as couples match, "R" positions, and not being ranked-to-match on both advanced and categorical rank lists were eliminated because they can result in a nonmatch despite truthfully ranking a program #1.

Conclusions: Each level of commitment statement resulted in a progressively increased frequency of a successful match to the recipient program. Only 5.4% of applicants committed to rank a program #1, but these statements were very reliable. These data can help program directors interpret commitment statements and assist accurate evaluation of the interest of candidates throughout the match process.

Download full-text PDF

Source
http://dx.doi.org/10.1213/ANE.0000000000004136DOI Listing

Publication Analysis

Top Keywords

commitment statements
36
guaranteed commitment
20
strong interest
16
commitment
14
statements
12
program directors
12
high rank
12
program
11
rank
8
interview season
8

Similar Publications

Introduction: The stakeholder analysis approach has historically been top-down rather than collaborative with key partners. However, this approach poses challenges for key partner engagement and community-engaged research, which aims to incorporate key partners throughout the project. This study, conducted by the Community Engagement Network at a Midwest Academic Medical Center, seeks to examine the value of community-engaged research for diverse key partners to increase collaboration, strengthen partnerships, and enhance impact, ultimately driving key partner engagement.

View Article and Find Full Text PDF

Objective: Urgent care centers (UCCs) have reported high rates of antibiotic prescribing for acute respiratory tract infections. Prior UCC studies have generally been limited to single networks. Broadly generalizable stewardship efforts targeting common diagnoses are needed.

View Article and Find Full Text PDF

Background: In a world confronted with new and connected challenges, novel strategies are needed to help children and adults achieve their full potential, to predict, prevent and treat disease, and to achieve equity in services and outcomes. Australia's Generation Victoria (GenV) cohorts are designed for multi-pronged discovery (what could improve outcomes?) and intervention research (what actually works, how much and for whom?). Here, we describe the key features of its protocol.

View Article and Find Full Text PDF

The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug side effects. In this study, we established a deep learning-based predictive model, utilizing a data set comprised of compounds known to either elevate or lower blood pressure.

View Article and Find Full Text PDF

Given the ubiquitous nature of love, numerous theories have been proposed to explain its existence. One such theory refers to love as a commitment device, suggesting that romantic love evolved to foster commitment between partners and enhance their reproductive success. In the present study, we investigated this hypothesis using a large-scale sample of 86,310 individual responses collected across 90 countries.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!