Perceptions of Brain-Machine Interface Technology among Mothers of Disabled Children
Bibliographic record
Abstract
Communication technologies are constantly transforming the way we communicate and interact with each other, and with our environment, with its impact affecting everyone including disabled people and the groups linked to them. The brain-machine interface (BMI) is one example of an emerging communication technology envisioned to transform the way we communicate and interact with each other and our environment in the near future. One group targeted to use BMI technology and impacted by others using BMI are disabled people. For disabled people and their families, the impact and implications of adopting BMI technologies is important to understand so they can make informed decisions and advocate for policies governing the technology's application to decrease negative and increase positive outcomes. In this study, we interviewed nine mothers of disabled children, with no prior knowledge of BMI technology, to explore their perceptions and attitude toward the technology. Five main themes emerged from our findings: the potential benefit to aid mothers to interpret their children's needs; the potential benefit to expand a child's social network; the preference for non-invasive BMI approach; impact of BMI use by non-disabled people and cost and qualification barriers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".