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The Correlation Between Students' Progress Testing Scores and Their Performance in a Residency Selection Process.

Pedro Tadao Hamamoto FilhoPedro Luiz Toledo de Arruda LourençãoAdriana Polachini do ValleJoélcio Francisco AbbadeAngélica Maria Bicudo
Published in: Medical science educator (2019)
Brazil is currently seeing an increased number of medical schools, leading to high competition for medical residency vacancies. Public managers have thus considered Progress Testing scores potentially useful as part of the final decision in the medical residency selection process. We analyzed whether there is a correlation between students' Progress Testing scores and their performances in medical residency selection. We examined four subsequent cohorts of students who attempted Progress Testing yearly and compared their accumulated scores with their medical residency selection scores from Botucatu Medical School, Universidade Estadual Paulista. We included 212 students who finished the 6-year medical course in 2013, 2014, 2015, and 2016. The comparison between the area under the Progress Testing curve and the medical residency selection score was performed using a Pearson correlation, with a p value set at < 0.05. We found a positive association between the two scores (p < 0.05 for the 4 years). Next, the students were grouped according to their performance in Progress Testing: above one, within one, and below one standard deviation. A chi-square test was used to compare the rates of approval with the second step of the medical residency selection process. Approval rates were 91.7%, 69.2%, and 42.1%, respectively (p < 0.05). We conclude that, in fact, there is a correlation between students' performance on these measures. This is partially explained by the fact that both instruments measure cognitive competencies and knowledge. These data may support national policy changes for medical residency selection.
Keyphrases
  • healthcare
  • high school
  • medical students
  • public health
  • mental health
  • machine learning
  • decision making
  • artificial intelligence
  • nursing students