What Motivates Managers to Coordinate the Learning Experience of Interprofessional Student Teams in Service Delivery Settings?
Bibliographic record
Abstract
This article addresses the realities of providing interdisciplinary student team placements (i.e., experiential team learning for students) in healthcare settings. Three site coordinators from different clinical settings in Alberta (a geriatric assessment unit, a geriatric dementia care unit, and a primary healthcare centre), who facilitated Student Team Placements from the University of Alberta (UofA) in 2004, comment on their experiences and incentives for participating in interdisciplinary teamwork with students. The coordinators suggest that students provide input into the sites' continuous quality improvement cycle, contribute to host organizations, and confer benefits for the student preceptors, the staff and the patients who participate. The site coordinators also recognize and accept the responsibility common to all service providers, to model a unique site culture that promotes learning/teaching of team skills for health science students. The experience of others in the literature supports our findings that two systems--the system to educate health professionals and the system that influences the health of the community--can interact so that each realizes a mutual benefit.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".