MétaCan
Menu
Back to cohort
Record W2742025923 · doi:10.1177/1087054717719535

An EEG Investigation of the Attention-Related Impact of Mindfulness Training in Youth With ADHD: Outcomes and Methodological Considerations

2017· article· en· W2742025923 on OpenAlexafffund
Annabel Sibalis, Karen Milligan, Carson Pun, Trish McKeough, Louis A. Schmidt, Sidney J. Segalowitz

Bibliographic record

VenueJournal of Attention Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsBrock UniversityMcMaster UniversityToronto Metropolitan University
FundersMitacs
KeywordsMindfulnessPsychologyAttentional controlElectroencephalographyTask (project management)Attention deficitsDevelopmental psychologyClinical psychologyCognitive psychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The current study examined the impact of an activity-based mindfulness treatment on EEG indices of attention in youth with ADHD aged 11 to 17 years compared with a waitlist control group. METHOD: Pre- and post-treatment, EEG was recorded as participants completed a single-point focus rest task and two active attention tasks. Theta power, beta power, and theta/beta ratio (TBR) were calculated during each task. RESULTS: A significant group by time by task interaction was found that indicated significant improvement in attentional ability, indexed by decreased TBR, for the treatment group but not controls. CONCLUSION: Findings support the benefit of mindfulness treatment for enhancing attentional control in youth with ADHD and extend the literature by providing evidence of these gains at a neural level. Findings also offer methodological support for the use of active attention tasks when examining mindfulness-related attentional gains in youth with ADHD. Directions for future research are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.403
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Attention DisordersSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207