Are mobile neurofeedback games a feasible way to improve self-regulation of attention for young marginalized children?
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".