Preliminary evidence for a general competency hypothesis.
Although predoctoral dental education is generally taught and evaluated by disciplines, there is no evidence bearing on whether the competencies necessary to begin independent practice are learned and practiced as a general set of skills, understanding, and values or as groups of discipline-specific skills, understanding, and values, which together constitute graduation competency. There is some support in the literature for each view In this preliminary investigation, 64,000 faculty ratings of student clinical competency were analyzed in a Year x Quarter x Discipline x Model design. The dependent variable was predictive validity of graduation quarter competency ratings using R-values from four prediction models. Results of a multiple repeated measures ANOVA show that models based on technical skills other than the one being predicted, clinical judgment and patient management, and the combination of these two models all predict graduation competency in each of four disciplines better than do ratings in the disciplines being predicted. As the time gap between predictive and predicted competence decreases, predictions become more accurate, but an asymptote is reached by the middle of the final clinical year. By using general models to evaluate students rather than discipline-specific ones, students needing intervention and remediation and those who could benefit from enrichment experiences can be identified as accurately at the beginning of their clinical careers as they can near the graduation deadline. This study provides preliminary support for a general competency hypothesis and suggests that research is necessary to better understand how students and dentists learn and practice rather than how they are taught.
Chambers
D W
DW
School of Dentistry, University of the Pacific, San Francisco, CA 94115, USA. dchambers@uop.edu
D000704:Analysis of Variance; D002140:California; D002983:Clinical Competence; D003162:Competency-Based Education; D003814:Dentistry, Operative / Q000193:education; D004497:Education, Dental / Q000379:methods*; D004708:Endodontics / Q000193:education; D005544:Forecasting; D006801:Humans; D017145:Models, Educational; D010517:Periodontics / Q000193:education; D011476:Prosthodontics / Q000193:education; D012044:Regression Analysis