A nationally representative sample of veteran and matched non-veteran college students: Mental health symptoms, suicidal ideation, and mental health treatment.

Objective To assess mental health symptoms, suicidal ideation/behaviors, and treatment among a nationally representative probability sample of student veterans. Participants: Student veterans enrolled in post-secondary educational institutions and matched comparison students. Methods: Sampled participants completed an online survey (n = 1,838). Analyses accounted for the complex sample design and non-response. Results: Substantial percentages of student veterans screened positive for: depression (36.9%, 95% CI: 31.1-42.7), PTSD (35.7%, 95% CI 29.9-41.5), anxiety (29.5%, 95% CI 26.8-32.2), and suicidal ideation (14.6%, 95% CI 12.1-17.1), with student veterans having odds ratios between 1.7 to 2.4 for positive screens compared to non-veteran students. Only 41.5% (95% CI 33.0-50.0) of student veterans with positive screens received treatment, although they had 50% higher odds of receiving treatment than non-veteran students. Conclusions: Student veterans have high rates of mental health symptoms and low rates of treatment. However, they are more likely to receive treatment than comparison students.

Predicting Medical School Enrollment Behavior: Comparing an Enrollment Management Model to Expert Human Judgment.

PURPOSE: Medical school admissions committees are tasked with fulfilling the values of their institutions through careful recruitment. Making accurate predictions regarding enrollment behavior of admitted students is critical to intentionally formulating class composition and impacts long-term physician representation. The predictive accuracy and potential advantages of employing an enrollment predictive model in medical school admissions compared with expert human judgment have not been tested. METHOD: The enrollment management-based predictive model previously generated using historical data was employed to provide a predicted enrollment percentage for each admitted student in the 2016-2017 application pool (N = 352). Concurrently, the human expert created a predicted enrollment percentage for each applicant while blinded to the values generated by the model. An absolute error for each applicant for both approaches was calculated. Statistical significance between approaches (expert vs. enrollment model) was assessed using t tests. RESULTS: The enrollment management approach was noninferior to expert prediction in all cases (P < .05) with a superior correct classification rate (77.7% vs. 71.2%). When considering subgroup analyses for specific populations of potential importance in recruiting (underrepresented in medicine, female, and in-state applicants), the enrollment management predictions were statistically more accurate (P < .05). CONCLUSIONS: Examining a single admitted class, the enrollment predictions using the enrollment management model were at least as accurate as the expert human estimates, and in specific populations of interest more accurate. This information can be readily exported for a real-time dashboard system to drive recruitment behaviors.

Enrollment Management in Medical School Admissions: A Novel Evidence-Based Approach at One Institution.

PURPOSE: In higher education, enrollment management has been developed to accurately predict the likelihood of enrollment of admitted students. This allows evidence to dictate numbers of interviews scheduled, offers of admission, and financial aid package distribution. The applicability of enrollment management techniques for use in medical education was tested through creation of a predictive enrollment model at the University of Michigan Medical School (U-M). METHOD: U-M and American Medical College Application Service data (2006-2014) were combined to create a database including applicant demographics, academic application scores, institutional financial aid offer, and choice of school attended. Binomial logistic regression and multinomial logistic regression models were estimated in order to study factors related to enrollment at the local institution versus elsewhere and to groupings of competing peer institutions. A predictive analytic "dashboard" was created for practical use. RESULTS: Both models were significant at P < .001 and had similar predictive performance. In the binomial model female, underrepresented minority students, grade point average, Medical College Admission Test score, admissions committee desirability score, and most individual financial aid offers were significant (P < .05). The significant covariates were similar in the multinomial model (excluding female) and provided separate likelihoods of students enrolling at different institutional types. CONCLUSIONS: An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.

Predictive efficacy of Chiropractic College Assessment Test scores in basic science chiropractic education.

OBJECTIVE: To evaluate the ability of Chiropractic College Assessment Test (CCAT) to explain academic success within a chiropractic basic science curriculum. METHODS: The CCAT examination was administered to 202 subjects from 1 chiropractic college on the first day of classes. Zero-order Pearson correlations were used to examine for associations between the prechiropractic grade point average (GPA), CCAT scores, and basic science GPA. Multiple regression techniques were applied to determine the predictive efficacy of CCAT scores on basic science GPA. RESULTS: Study results indicate a correlation between prechiropractic GPA, CCAT scores (r = 0.348, P < .001), and basic science GPA (r = 0.559, P < .001). Correlation was also noted between CCAT scores and basic science GPA (r = 0.537, P < .001). Using multiple regression, together the variables (age, postsecondary education, prechiropractic GPA, and CCAT scores) accounted for a significant portion (R2 = 0.483, P < .001) of the total variance in basic science GPA. Furthermore, the CCAT scores accounted for significant unique explanation (change R2 = 0.081, P < .001) beyond that offered by the traditionally used prechiropractic GPA. CONCLUSION: The CCAT examination provides a valuable a priori indicator of success within the basic science curriculum of this particular chiropractic program. Consideration should be given to adopting the CCAT examination as one of a number of heuristic guides students and college officials use in making enrollment decisions.