An academic and free clinic partnership to develop a sustainable rural training and clinical practice site for the education of undergraduate and advanced practice nurses
Bibliographic record
Abstract
The purpose of this project was: 1) to expand clinical training experiences for undergraduate, graduate and advanced practice nursing students at a rural free clinic, 2) to test the feasibility of developing a model training and practice internship for undergraduate, graduate and advanced-practice nurses as part of a Health Resources and Service Administration (HRSA)-funded academic-community partnership to encourage nurses to consider future employment in rural Appalachia and 3) to determine the successes and challenges of this endeavor. This paper reports the successes and challenges of this partnership. Data were collected from nursing students attending the University of Virginia through self-reported student information forms. A total of 145 students (56 advanced practice, 19 graduate and 70 undergraduate nursing students) successfully received scheduled clinical training experiences at three rural clinic sites operated by the Health Wagon (HW), a free clinic in rural Southwest Virginia. It is feasible to develop and implement a long distance academic and community-based partnership to provide real life experiences for undergraduate, graduate and advance practice nurses, including nurse practitioners, in rural settings. Success depends on the commitment of both the academic and free clinic staff to the program, excellent on-site clinical supervision of students, and a source of revenue to cover both on-site and travel related expenses for students and preceptors.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".