Nurse faculty shortage problem and a grant solution to the problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: The projected growth of the nurse workforce will drive the need for more nurse faculty in the U.S., with an estimated needed increase of 24% (16,000 new faculty) to meet workforce demands between 2016 and 2026. However, nursing schools have struggled to keep pace with growing enrollment demands and faculty retirements.Methods: To meet the needs of the nursing workforce and to address the nursing shortage by educating qualified faculty, the University of Cincinnati College of Nursing (UC CON) has applied for and received funding through the federal government’s Health Resources & Services Administration (HRSA) Nurse Faculty Loan Program (NFLP) grant.Results: Since 2012, the UC CON has utilized the NFLP to provide nearly $4.4 million in loans to students. The UC CON has supported 115 individual graduate-level nursing students with NFLP funds. As of 6/07/24, 73 UC CON degrees have been awarded. Several prior recipients are working as nurse faculty, some at rural and community colleges where the nurse faculty shortage is most acute. Some of the students who received NFLP funds and graduated are working in medically underserved and/or rural areas across the U.S.Conclusions: A new grant submission was funded in June 2024 to fund more doctoral students and Master of Science in Nursing Education Students. The UC CON provides additional components (e.g., nurse educator development, professional development, and job search support) to strengthen recipients’ preparation and marketability for nurse faculty roles in all regions, including rural and underserved areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it