Multiple stakeholder perceptions of assistive technology for individuals with cerebral palsy in New Zealand
Bibliographic record
Abstract
BACKGROUND: This study sought to gain an understanding of the experiences and perspectives of assistive technology from different stakeholders in technology adoption, in the New Zealand context. METHODS: A focus group was held with individuals with cerebral palsy (n = 5), service providers (n = 4), caregivers (n = 3) and a biomechanical engineer. The data recordings from the focus group were transcribed and coded using thematic analysis. RESULTS: Themes emerged around barriers imposed by the assessment process and training in assistive technology procedures, the influence of family members, the environment that assistive technology is used in, and psychosocial aspects of being able to participate and integrate into society. CONCLUSION: The results are similar to other literature, suggesting new innovations and changes are in dire need, to improve assistive technology experiences for all stakeholders. Implications for Research Service providers for assistive technology desire more effective training and support of existing and emerging technologies. Although the set procedure for acquiring assistive technology in New Zealand is comprehensive, incorporating multiple perspectives, it is difficult to follow through in practice. More innovative procedures are needed. The movement of Universal Design is significantly improving the perception of individuals with disabilities, and has enabled greater social inclusion. More assistive technology developers need to ensure that they incorporate these principles in their design process.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".