An Innovative, Arts-Based Approach to Interprofessional Education
Bibliographic record
Abstract
Obesity is a global health concern that is challenging to address at the health system level. This is partly because health professionals perceive themselves to be poorly equipped to effectively handle weight management issues. Compounding this are the biases held by health professionals towards clients living with excess weight. This project aimed to address these biases among health professionals through the use of live, dramatic arts as a pedagogical tool to disseminate findings from an original research study that explored multiple health system perspectives on weight management. Using an interprofessional learning format, the research team facilitated four, interprofessional education (IPE) workshops in universities across Atlantic Canada with health professional students and their faculty. Post-workshop evaluations indicate that the workshop was well received; the live, dramatic presentation of professional-client perspectives on obesity management was effective for not only facilitating understanding about the original research findings, but in provoking thought about issues of weight management and providing an opportunity for attendees to engage in interprofessional learning. The majority of participants perceived that this workshop would positively benefit them in their future work as health professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".