Humour: A Bridge to Interprofessional Learning: IP.Global Café International Presentation
Bibliographic record
Abstract
Date and Time: 23 September 2022; 12:00 GMT/UTC. 55 Attendees from 7 Countries: England, Ireland, Norway, Sweden, Australia, Netherlands, and Canada. Presenter: Dr Vikki Park, Assistant Professor of Interprofessional Education and Collaborative Practice, Northumbria University (UK) \nWhilst researching interprofessional learning (IPL) culture in the acute environment of adult critical care, humour emerged as a key influential factor that affected learning between different professions. It served many functions and when used effectively humour could increase rapport between workers, creating trust and psychological safety which made it easier to ask questions. These moments enhanced opportunities for IPL and the increased dialogues often resulted in the co-creation of knowledge between members of the healthcare team. In this IP.Global Café event, Dr Vikki Park provides insight to the findings of her focused ethnographic research with respect to the role of humour and IPL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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; both teacher heads agree on what is shown here.
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".