Problem Based Learning: Developing competency in knowledge integration in health design
Bibliographic record
Abstract
Different communities, organizations, and people hold different views on their own and others’ wellbeing. It is often challenging to balance different perspectives during the design process when the truth of medicine is competing with the truth of social media and the everyday experience of wellbeing of patients, caregivers, family and friends. In the context of the Masters of Health Design at OCAD University (OCAD U), we develop students’ competency in working with truth through challenging students to engage with multiple ‘truths’ in the design process, engaging deliberately in identifying and working with multiple truth regimes as part of a problem based learning approach. This includes how truth regimes impact the understanding of a challenge area, techniques for engaging with stakeholders, communicating and developing concepts, and the process of seeking and working with feedback for refining and iterating, and finally in communicating project solutions. By engaging in problem based learning, students are exposed to the real challenges of different stakeholder perspectives and in particular how different truth regimes serve to impact what counts as legitimate knowledge and legitimate knowledge representation.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".