Community Partnership Interprofessional Program as Pedagogy: Process Outcomes and Faculty Impressions
Bibliographic record
Abstract
Background: Since 1992, East Tennessee State University (ETSU) has augmented traditional health professions curricula with community-based, experiential learning through the Community Partnership Interprofessional Rural Health Program. The program was expanded in 2005 by including more interprofessional faculty, students, and community partners. Interprofessional teams of students and faculty work with community organizations to identify health needs and assets and implement health education programs or services.Methods and Findings: Course process outcomes were compiled from a survey of section reports and presentations. Faculty impressions of being involved in the course were gathered through conducting interviews with five interprofessional faculty. From 2005–2011, community partners included individuals, groups, and organizations within seven counties in Tennessee. Forty programs and services have been implemented through the program during the past seven years. Faculty reported the main reasons for being involved are their interests in interprofessional education and working in communities. Faculty also cited 12 different types of teaching strategies (pedagogical approaches) employed through the course.Conclusions: The Community Partnership Interprofessional Rural Health Program at ETSU is a testing ground for the unique combination of communitybased learning and interprofessional health education. Study findings demonstrate how the course has benefited faculty, students, and communities.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".