Predictors of student success in an entry-level baccalaureate dental hygiene program.

PURPOSE The purpose of this study was to measure the utility of various predictors used by the Old Dominion University Gene W. Hirschfeld School of Dental Hygiene baccalaureate degree dental hygiene program in selecting dental hygiene students who are most likely to graduate and be successful in passing the National Board Dental Hygiene Examination (NBDHE). The following factors were examined: grade point average (GPA); science GPA; final grade in various prerequisite courses; final grade in first-year dental hygiene courses; academic setting where prerequisite courses were completed; multiple attempts to achieve a passing course grade; and admissions criteria points (ACP). METHODS The sample selected for study consisted of the academic records of dental hygiene students admitted to the program from 1998 to 2002 (n = 235), who would have been eligible to take the NBDHE from 2000 to 2004. Data were analyzed using multiple logistic regression to determine success as measured by graduation (n = 146). With NBDHE as the criterion variable, data were analyzed using the multiple linear regression to determine successful entry into the profession (n = 130); significance was predetermined at the 0.05 level. RESULTS Data analysis revealed that final course grade in oral pathology was a significant predictor of successful graduation (P = 0.0008). Variables that predicted NBDHE success were final course grade in oral pathology, final course grade in oral anatomy and histology, and the ACP rating (P < .0001, P < .0001, and P = .0245, respectively). There was no statistically significant relationship for other variables. CONCLUSION Final grades in oral pathology and oral anatomy and histology can significantly predict graduation and NBDHE success at this institution, suggesting that educators look to improving student performance after admission to the program to improve the likelihood of success. Additionally, when this institution's admission variables were combined into a cluster of variables (ACP), they proved significant at predicting success.