SMHS to present Master of Physician Assistant Studies degrees
Bibliographic record
Abstract
GRAND FORKS, N.D.— The Department of Physician Assistant Studies Hooding Ceremony for the Master of Physician Assistant Studies Class of 2016 at the University of North Dakota School of Medicine and Health Sciences will take place on Friday, May 13, at 2:00 p.m. at the Gorecki Alumni Center. Official diplomas will be granted during University of North Dakota Commencement on Saturday, May 14. Thirty-three students will receive their Master of Physician Assistant Studies (MPAS) degree. The program comprises a 24-month curriculum and includes a combination of online coursework, classroom experiences on campus and clinical experiences under the supervision of a physician or physician assistant in rural or underserved primary care areas. The primary mission of the University of North Dakota Department of Physician Assistant Studies is to prepare selected students to become competent physician assistants working collaboratively with physician supervision, emphasizing primary care in communities within North Dakota as well as regionally, nationally, and globally. With this mission, the goal is to improve access to healthcare, help alleviate shortages of primary care providers, and deliver quality, affordable, and comprehensive healthcare to rural or underserved populations. To date, the program has 1,780 graduates who are employed throughout the United States (including Hawaii, Alaska, and the Virgin Islands), Canada, and overseas. The program’s success in meeting its mission is evidenced by the following: 67 percent of recent graduates are employed in primary care practices; 78 percent of recent graduates reside in the same city or town as when they graduated (trained and retained as medical providers in home areas); and 53 percent of recent graduates are practicing in a rural area (defined as fewer than 25,000 people). University of North Dakota President Edward Schafer; UND Vice President for Health Affairs and Dean of the School of Medicine and Health Sciences Joshua Wynne, MD, MBA, MPH; and SMHS Senior Associate Dean for Education Gwen Halaas, MD, MBA, will offer welcoming remarks along with Department of Physician Assistant Studies Chair Jeanie McHugo, PhD, PA-C. Associate Dean for Health Sciences Tom Mohr, PT, PhD, will deliver closing remarks. Awards will be given to academically outstanding students, committed preceptors, and students who performed well on their scholarly projects. Students will be hooded by their family and friends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.288 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".