From patient to maker - a workflow including people with cerebral palsy in co-creating assistive devices using 3D printing technologies
Bibliographic record
Abstract
Purpose: Digital fabrication, like 3D printing, is a new opportunity for rehabilitation professionals to produce customized assistive devices. It allows for empowerment and collaboration in device procurement, but practical implementations are scarcely described. We describe the workflow, discuss feasibility and propose directions for future work.Methods: We showcase a process of co-manufacturing a custom spoon handle together with two people with cerebral palsy. Our digital manufacturing process was centered around videoconferencing to remotely control the processes from design to final 3D printing. Device functionality and satisfaction were assessed using standard clinical questionnaires: the Individual Priority Problem Assessment Questionnaire (IPPA) and the Quebec User Satisfaction Assessment with Assistive Technology (QUEST 2.0).Results: IPPA was instrumental in assessing user needs and device effectiveness. QUEST revealed where to focus future design efforts.Conclusion: Involving people with disabilities in co-creation of assistive devices opens for new opportunities for healthcare providers that should be explored in depth using the described methodology. There may also be therapeutic benefits and we envisage specific actions to take in order to make it clinically viable.IMPLICATIONS FOR REHABILITATIONBest practices for co-creation of assistive devices, cost and benefits should be investigated and documented further.Standard questionnaires are useful for measuring effectiveness and satisfaction of co-created devices as well as for guiding design efforts.Co-creation may be a valuable element in therapeutic interventions as an opportunity to unfold creativity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".