Implementing and Sustaining a Rural Interprofessional Clinical Education Program
Bibliographic record
Abstract
Background: After a successful pilot project introducing interprofessional (IP) clinical education in a rural hospital, expansion to other rural hospitals was attempted. Despite enthusiasm for the pilot project and funding, the university-based project team had difficulty persuading administrators and staff to become involved or to maintain the project. Of 9 institutions, 2 implemented and sustained the project for more than 2 years, 2 initiated but dropped it, and 5 declined.Methods and Findings: A qualitative, interpretive description study was conducted to identify facilitators and barriers to implementing an IP clinical education program in rural settings. Semi-structured interviews were conducted with representatives of organizations that sustained the project, dropped out, or never participated.Using the National Health Service Sustainability Model we identified the staff, organization, and process factors that affected the program implementation. Three staff roles were required for success: sponsor, champion, and gatekeeper. Organizational factors included infrastructure to identify participants and perceived project enhancement of organizational values. Process factors included organizational benefits, compatible priorities, and adaptability.Conclusions: Introduction of IP education to rural institutions requires complex combined factors. However, continuation of the project at two sites demonstrates that when IP education is valued and sustainability factors are present, staff will maintain it.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".