The REHAB-LAB model for individualized assistive device co-creation and production
Bibliographic record
Abstract
Assistive devices are designed to enhance individuals with disabilities' functional abilities. The rise of 3D printing technology enabled the production of individualized assistive devices (IADs). A REHAB-LAB is intended for IAD provision involving technical referents and occupational therapists. This study aimed to develop the REHAB-LAB logic model; to explore its fidelity and desirability; and to explore the characteristics of arising initiatives of IAD production. The REHAB-LAB logic model development involved stakeholders throughout the research process. A pragmatic multimethod approach followed two phases 1) logic model development and 2) exploration of its fidelity and desirability. The REHAB-LAB logic model presented the resources (equipment, space, human) required to implement IAD provision in a rehabilitation center, and the expected deliverables (activities and outputs). The REHAB-LAB logic model highlights the interdisciplinarity of IAD provision including occupational therapists, doctors, engineers, managers, and technical referents and places the users at the center of the IAD production. Results confirmed the fidelity and desirability of the REHAB-LAB logic model. The REHAB-LAB logic model can be used as a reference for future healthcare organizations wishing to implement an IAD provision. This research highlighted the interest of IAD provision based on the REHAB-LAB model involving users and transdisciplinary practices.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".