Using IPEC pedagogy to transform the future rural advanced practice nursing workforce
Bibliographic record
Abstract
Objective: The number of primary care providers has not kept pace with the increasing number of underserved rural populations placing unprecedented demands on the healthcare system and the gap is expected to widen with shortages projected to increase across the United States. Given the urgent need to grow and expand the number of trained diverse primary care providers in rural communities, an innovative sustainable program was implemented to recruit and train diverse rural advanced practice nurses. Building on the successful rural medical and rural pharmacy educational programs at the UIC Health Sciences Campus in Rockford, a rural nursing program with interprofessional curriculum was designed and refined to enable nursing students along with two other professions to develop appreciation, insight, and knowledge of rural healthcare and health disparities in a variety of rural settings as part of an interprofessional team.Methods: A mixed-methods program evaluation approach utilized both quantitative and qualitative data to evaluate program satisfaction and inform ongoing program refinement.Results: Students indicated positive responses to this interprofessional course of study. Continued development and refinement of the curriculum is planned to train the future rural healthcare workforce.Conclusions: Students from three health sciences colleges benefitted from the IPEC program with confirmed satisfaction in interprofessional rural education and collaborative practice. The addition of a rural nursing program merits continuation with modification and expansion to prepare the future rural interprofessional healthcare workforce.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".