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Record W4402526699 · doi:10.1016/j.ijcci.2024.100690

Are mobile neurofeedback games a feasible way to improve self-regulation of attention for young marginalized children?

2024· article· en· W4402526699 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Child-Computer Interaction · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeurofeedbackPsychologyCognitive psychologyComputer scienceDevelopmental psychologyElectroencephalographyNeuroscience

Abstract

fetched live from OpenAlex

Interactive technology-mediated behavioral interventions are increasingly being studied with children at risk for attentional challenges. Few technology-mediated interventions have been designed for, or studied in the field with, marginalized children, who are at an elevated risk for attentional challenges. We adapted three existing neurofeedback games to create a proof-of-concept intervention to address this research gap. To investigate preliminary feasibility and efficacy we conducted a controlled field experiment with 28 children (aged 5 to 8, 22 male) from a disadvantaged community. Findings showed that with support all children were able to complete the intervention, and most were able to transfer newly attained attention regulation skills into everyday situations and maintain those skills over time. Our work serves as a proof-of-concept for this type of technology-mediated mental health intervention research, provides an exemplar of digital health research with hard-to-reach populations, and provides preliminary evidence that this research space warrants future attention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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