3D printing as assistive technology for individuals with deafblindness: perspectives of rehabilitation professionals
Bibliographic record
Abstract
There is a growing body of evidence on practical applications of three-dimensional (3D) printing to support the rehabilitation of individuals with sensory impairments. However, applications in the field of deafblindness, or the combination of vision and hearing impairment, remain scarce. Therefore, the present study aimed to explore actual and potential applications of 3D printing in deafblindness rehabilitation from the perspective of rehabilitation professionals in two focus group discussions that involved orientation and mobility specialists, vision rehabilitation specialists, audiologists, and braille technicians. Participants exchanged on 1) 3D printing applications to address their clients' rehabilitation needs, 2) factors that can impact its integration into their practice, and 3) the ideal logistics for producing and delivering 3D printed products. Educative models and functional adaptations were identified to improve communication, learning, mobility, and independent living skills for individuals with deafblindness. Professionals agreed that the main barriers limiting 3D printing adoption were linked to time constraints and insufficient awareness or knowledge about this technology, while the most crucial facilitator was the promotion of interdisciplinary collaborations with 3D printing experts. The present findings thus emphasize the need for global collaborations, knowledge dissemination, and ongoing research and validation of 3D printing applications to support individuals with deafblindness.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".