Strategies for success that led to 99.98% school of nursing retention
Bibliographic record
Abstract
Internationally, every year, thousands of students begin their journey in higher education by enrolling in pre-licensure and advanced nursing programs. However, not all students successfully complete their degrees to fruition. According to the National League for Nursing (NLN), the average national dropout rate for nursing programs in the United States is 20%-25%; this high attrition rate is considered problematic. The purpose of this presentation is to present the successes of a School of Nursing within Mississippi that has led to a 0.02% attrition/99.98% retention rate of students within programs. Some strategies proven to be successful in retaining students are: student support services, technical/tech support programs, online and face-to-face orientations, and student connection sessions set up for building peer relationships. Faculty and students are often unaware of the services provided by the institution and their department/school; dissemination of these services is pivotal to facilitating student success. The World Health Organization has predicted a shortage of over 18 million healthcare professionals by 2030, with half of those individuals representing the nursing profession. It is imperative to understand the attrition and retention of nursing students to prevent further loss of the future healthcare workforce. University of Mississippi Medical Center School of Nursing focuses on various processes that contribute to its success for the retention of nursing students at all levels. Percentile of attrition from Summer and Fall 2022 semester enrollment, metrics revolving around student leave, and demographics of the school's programs/state of Mississippi are reviewed in this paper. The success of the SON program in gaining high retention is multifactorial. A detailed outline of how academic affairs, student affairs, and administration work together to achieve a culture of success for students within the School of nursing is presented in this manuscript.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".