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An Internet-based tool for evaluating third-year medical student performance.

Abstract

BACKGROUND: Shortened non-primary care medical school clerkships have increased time pressures for accurate assessment of student knowledge, skills, and attitudes. Paper-based student evaluations suffer from low response rates, inefficient data acquisition and analysis, and difficulty obtaining input from multiple evaluators. This project describes the development of a Web-based model for evaluating third-year medical student performance, improving evaluation response rates, and including multiple evaluators' input. METHODS: A secure Web-based system was designed to maintain evaluation data (11-item competency-based evaluations, oral examinations, National Board of Medical Examiners surgery test, and objective structured clinical examination) for the third-year surgery clerkship. Historical response rate, completion time, and administrative effort data were compared with data obtained using the Web-based model. RESULTS: Faculty response rates improved from 71.3% to 89.9%, with response times decreased from 28.0 +/- 3.0 to 9.0 +/- 0.7 days using the Web-based model. Administrative time requirements decreased from 5 days to 2 hours per rotation, and manual data entry, analysis, and reporting were eliminated through e-mail evaluator notification, direct data entry, and real-time analysis. Evaluator satisfaction was subjectively higher using the Web-based model. CONCLUSIONS: The Web-based 360-degree evaluation model improves third-year medical student assessment by including residents, reducing time and cost, and by providing a faster, more inclusive, and efficient evaluation.

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BibTeXRIS

Scott R Schell, D Scott Lind. 2003. An Internet-based tool for evaluating third-year medical student performance.. https://doi.org/10.1016/s0002-9610(02)01359-4

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Conditional reliability of admissions interview ratings: extreme ratings are the most informative.

CONTEXT: Admissions interviews are unreliable and have poor predictive validity, yet are the sole measures of non-cognitive skills used by most medical school admissions departments. The low reliability may be due in part to variation in conditional reliability across the rating scale. OBJECTIVES: To describe an empirically derived estimate of conditional reliability and use it to improve the predictive validity of interview ratings. METHODS: A set of medical school interview ratings was compared to a Monte Carlo simulated set to estimate conditional reliability controlling for range restriction, response scale bias and other artefacts. This estimate was used as a weighting function to improve the predictive validity of a second set of interview ratings for predicting non-cognitive measures (USMLE Step II residuals from Step I scores). RESULTS: Compared with the simulated set, both observed sets showed more reliability at low and high rating levels than at moderate levels. Raw interview scores did not predict USMLE Step II scores after controlling for Step I performance (additional r2 = 0.001, not significant). Weighting interview ratings by estimated conditional reliability improved predictive validity (additional r2 = 0.121, P < 0.01). CONCLUSIONS: Conditional reliability is important for understanding the psychometric properties of subjective rating scales. Weighting these measures during the admissions process would improve admissions decisions.

Education, Medical, Undergraduate↗