Transformative Design: From Consultant to Clinician
Bibliographic record
Abstract
The paper will describe how digital information gathered in medical diagnostic practices has been utilized in an area traditionally reliant on manual medical sculpting techniques. Working in conjunction with iRSM (Institute for Reconstructive Sciences in Medicine), the authors have participated in the development of systems and processes that have resulted in: enhanced surgical planning, elimination of surgeries and improved accuracy of prosthetics. As iRSM is the only centre in Canada to provide these services, it has attracted many international medical facilities and practitioners to use these digital workflows in their own practice. Industrial Designers were originally consulted by iRSM on a project-by-project basis, consistently demonstrating the value of design research strategies. This demonstration of value resulted in the demand for a full-time designer within iRSM’s interdisciplinary team, opening new insights and opportunities within the clinical environment. The integration of design into this area of medicine resulted in the development of a new field of design interaction, education and research. The collection of numerous case studies over an eight-year period provided the background for the development of a graduate program of study dedicated to this new field. The result of this work has been presented exclusively within the medical arena both at conferences and workshops. This academic year, the first student to be enrolled in a Master of Science in Rehabilitation Medicine with a specialization in Surgical Design and Simulation came to fruition. The creation of this new field of study is a continuation of this relationship, as the first candidate has a degree in Industrial Design, but will gain the necessary skills to become a clinician and researcher within a clinical practice. This new “species” of designer is at the forefront of new opportunities for design education and research with a focus on patient-centered health care delivery.
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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 | 0.029 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.056 | 0.021 |
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".