Universities preparing health professionals for the 21st century: Can something new come out of the traditional establishment?
Bibliographic record
Abstract
To address clinical education in chronic disease management, the University of Alberta partnered with the local health authority to develop interprofessional team placements in diabetes chronic disease management. The objectives were to determine a meaningful and sustainable approach to interprofessional team placements. Pre-licensure students from medicine, nursing, nutrition and pharmacy participated in a project-based team experience designed to increase their discipline-specific knowledge of diabetes while enhancing their team process skills. Quantitative and qualitative research methods were used to evaluate project implementation and student outcomes. The majority of students reported learning "a great deal" about the roles of other health professionals and knowledge regarding the management of diabetes from the perspective of other health disciplines. However, several key learning outcomes were not fully accomplished; to enhance student's interprofessional team skills, increase their knowledge of interprofessional team functions and learn how to modify team function in a clinical setting. Creativity concerning relevant projects, varying settings, and differing models of service delivery should be encouraged, and flexibility within the project and process is essential for self-directed learning of student teams. New models of interprofessional clinical education, such as the chronic disease management model used in this study, have potential, if supported by the shift in health science programs culture.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".