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.v16i2.4176 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.Three nursing and four medical students who agreed to undertake rural preceptorships were recruited through their supervising faculty.Upon their placement, three registered nurses and four physicians, assigned to precept the students, also agreed to take part.The researchers collected data through midpoint and endpoint semi-structured interviews, and two focus groups.The data were coded and analysed using Glaserian grounded theory.In the participants' view, successful rural IPE resulted in enhanced knowledge of each others' scope of practice, firsthand insights into rural IP teamwork and increased confidence in working with other disciplines.Authenticity was a key distinguishing rural feature.Success moreover depended on buy-in and facilitation by preceptors and staff, and student initiative and selfreliance.Hinderances to success were lack of logistical support, professional inertia or turfconsciousness, and student discomfort with IP engagement.Over the course of the pilot, the students grew to emulate their rural preceptors' interprofessional collegiality beyond the clinical setting.The results of this study support the implementation of IPE in clinical rotations as an alternative or an adjunct to classroom-based IPE.The rural context may be particularly advantageous to clinical IPE owing to comprehensive coverage of acute care and holistic, community focus.
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".