Empowering Individuals with Limited Joint Mobility: An Embedded Interdisciplinary Program between Occupational Therapy & Industrial Design
Bibliographic record
Abstract
This paper highlights the third consecutive year of an ongoing, embedded interdisciplinary collaborative program between Occupational Therapy Doctoral (OTD) and Master of Industrial Design (design) students and faculty co-creating assistive devices that improve participation in tasks and activities for individuals living with Fibrodysplasia Ossificans Progressiva (FOP). FOP is a rare and progressive genetic disease, causing an individual's muscles, tendons, and ligaments to turn to bone, resulting in fixation and immobility. During the 9-month collaborative experience, OTD and design students co-conducted 2 sets of interviews with FOP clients identifying activities of daily living that were problematic secondary to individual disease course. OTD students administered the Canadian Occupational Performance Measure (COPM) to identify which daily routines and activities were impacted. Design students employed standardized questions to clarify how their activities were performed. Findings informed initial assistive device prototypes, which were then sent to FOP clients for user testing. After testing was completed, design students continued to prototype and conducted a second feedback interview. This program also included several interprofessional educational experiences designed by the OTD students for their design peers under occupational therapy (OT) and design faculty supervision. Content was created to reinforce FOP clients’ needs and was delivered through shared learning modules, activities, and peer-to-peer discussion. Weekly collaboration occurring in design coursework between disciplines and use of OT-related tools such as the COPM helped the design students leverage their skills by contextualizing what they observed when interacting with FOP clients, ultimately creating better products that met the needs of individuals with FOP. Findings presented include current data up to February 2022 from OTD and design interviews and FOP user feedback; data collection and prototyping will continue through May 2022. Outcomes demonstrated and reinforced the need for consistent, higher-level embedded interdisciplinary collaborative approaches that uniquely meet the needs of clients experiencing complex medical issues and help to expand the usability of assistive devices for other populations with complex issues.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".