A Rural Interprofessional Educational Initiative: What Success Looks Like
Bibliographic record
Abstract
The researchers implemented an interprofessional education (IPE) pilot program, wherein final year baccalaureate nursing students and 3rd year medical students undertook preceptorships concurrently in a semi-rural acute care setting. The goal was to emphasize interprofessional (IP) collaboration and team-building. The researchers sought to determine how hands-on, clinically-Online Journal of Rural Nursing and Health Care, 16(2) http://dx.doi.org/10.14574/ojrnhc.v16i2.417 6 based IP experiences could improve classroom-based IPE, and how the rural context might mediate such experiences. This article is an exploration of how participants defined a successful rural IPE experience, and which factors promoted or hindered that success. The definition of rural as used in the context of the research is to live outside of a major city.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".