Reflections from an interprofessional education symposium on the use of art to inform clinical practice
Bibliographic record
Abstract
A novel, student-organized event, the ‘Art in Healthcare’ interprofessional education symposium was held in November 2018 as the inaugural event hosted by the Windsor Interprofessional Health Student Collaboration. Students attending represented five different programmes of study and came from five different campuses, all in Ontario. The impetus for it was grounded in the existing landscape of interprofessional education and the use of narrative and artistic approaches to guide reflection on professional identity for health professionals. The structure of the symposium included a keynote address, workshops, and a closing ceremony. Pre- and post-symposium surveys were administered and filled out by students and helped to inform this reflection. Participants were given the space, time, and artistic tools to engage in critical thought about their past experiences as health care professional students. They used narrative and artistic approaches to express complex and difficult thoughts and ideas which helped to illuminate shared experiences and create shared awareness. Through reflection and conscious decisions regarding representation of ideas through alternative artistic media, students explored their feelings and identities. The ‘Art in Healthcare’ symposium introduced new tools and methods for health professional students to engage in critical reflection, providing many benefits for students and their patients alike.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.013 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 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".