Partnering to Enhance Interprofessional Service-Learning Innovations and Addictions Recovery
Bibliographic record
Abstract
CONTEXT: Service-learning programs are reported to benefit students, faculty, higher education institutions, community agencies and the relationships among these groups. An interprofessional service-learning paradigm may strengthen these benefits. Community settings can expose students to social and cultural determinants of health, in addition to those biomedical determinants more commonly addressed in health sciences curricula. These experiences can also enhance student understanding of the complexities underlying treatment and prevention of modern health problems, particularly chronic diseases. OBJECTIVE: The purpose of this initiative was to create and deliver interprofessional service-learning innovations that would enhance student learning and addictions recovery. To address this initiative, the University of Washington's Health Science Partnerships in Interdisciplinary Clinical Education (HSPICE) and the Salvation Army Adult Rehabilitation Center (ARC) began a community-campus partnership in 1997. Innovations took into account student educational objectives established by HSPICE which included: participation in interdisciplinary teams, in conjunction with community partners to identify and reduce population-based health issues, realization and articulation of biases regarding issues faced by the participating community, acquiring an understanding of the broader determinants of health and developing an understanding of why the complexity of population health requires interdisciplinary strategies for cost effectiveness. DISCUSSION: Findings are reported from evaluations, needs assessments and ongoing feedback of men recovering from addictions, as applied to health education materials and presentations developed for the ARC. Future directions are highlighted, including the need for further research and evaluation efforts aimed at rigorously assessing cost savings and student knowledge, skills and cultural sensitivity, among others.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".