Academic Degrees and Clinical Practice Characteristics: The University of Washington Physician Assistant Program: 1969-2000

CONTEXT: The physician assistant profession has been moving toward requiring master's degrees for new practitioners, but some argue this could change the face of the discipline. PURPOSE: To see if there is an association between physician assistants' academic degrees and practice in primary care, in rural areas, and with the medically underserved. METHODS: Surveys were sent to 880 graduates of the first 32 University of Washington physician assistant classes through 2000. Respondents noted their academic degree at program entry and the highest degree attained at any time up to the time of survey. Relationships between practice characteristics and academic degree levels were tested by unadjusted odds ratios and logistic regression after controlling for year of graduation and sex. RESULTS: Of the 478 respondents, 54% worked in primary care, about 30% practiced in nonmetropolitan communities, and 42% reported providing care for the medically underserved. Respondents with no degree (33% of total at entry, 24% at survey) were significantly more likely than degree holders to work in primary care and nonmetropolitan areas. Respondents with no degree at program entry were significantly more likely, and those with no degree at the time of the survey were marginally more likely, to self-report work with the medically underserved. CONCLUSION: Respondents with no academic degree are significantly more likely to demonstrate a commitment to primary, rural, and underserved health care. These findings may inform the national debate about the impact of required advanced degrees on the practice patterns of nonphysician providers.     

A new index for evaluating horizontal skeletal discrepancies and predicting treatment outcomes

The cephalometric measurement known as Wits appraisal (AOBO) was studied to determine its importance in diagnosis, treatment, and prediction of treatment success. The Wits appraisal is used primarily to identify unreliable ANB values (those that do not accurately reflect the degree of horizontal jaw discrepancy, ie, those that differ greatly from the AOBO value). Therefore, differences between AOBO and ANB also were evaluated. Because neither measurement alone can assess jaw discrepancies with total accuracy, they were combined, plotted on a scattergram, and divided into equal zones to create a new index of horizontal skeletal discrepancies and treatment predictability. Pretreatment values correspond to treatment difficulty, and posttreatment values correspond to the effectiveness of treatment. Treatment time for each zone also was evaluated. We were able to draw several conclusions from this study. First, combining AOBO and ANB values results in a more accurate measurement of treatment difficulty. These combined values also can be used to predict both treatment time and the success of treatment for horizontal discrepancies. Second, the AOBO value predicts variations in treatment time at each ANB value. Finally, adding ANB and AOBO values and then dividing the total yields zones that represent statistically significant differences in treatment times and percentages of success. The zones also correlate with the difference between AOBO and ANB measurements.