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Record W2888084024 · doi:10.1080/2326263x.2018.1505191

Caregiver and special education staff perspectives of a commercial brain-computer interface as access technology: a qualitative study

2018· article· en· W2888084024 on OpenAlex
Sarvnaz Taherian, T. Claire Davies

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrain-Computer Interfaces · 2018
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsQueen's University
Fundersnot available
KeywordsInterface (matter)Brain–computer interfaceQualitative researchHuman–computer interactionPsychologyComputer scienceMedical educationSociologyMedicineNeuroscienceOperating systemSocial science

Abstract

fetched live from OpenAlex

This study sought to understand the perceptions of special education staff and caregivers (n = 6) who took part in a brain-computer interface (BCI) technology trial for individuals with severe cerebral palsy. Participants were interviewed post-trials regarding the different BCI components. The transcripts were coded and analyzed using thematic analysis. Results showed that BCIs are not suitable for independent use outside of clinical/laboratory settings. The hardware needs to be configurable, comfortable and accommodate physical support needs. The training approach needs to be less cognitively demanding, motivating and support personalized mental tasks. For BCIs to transition into the real world, there should be adequate technological support, improved reliability, and a systemic assessment of how the technology will fit into the lives of end users. Participants emphasized the on-going need to involve users and individuals who support them, to create a system that truly meets the needs of the users.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.374
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it