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Record W1636120922 · doi:10.1177/1087054715594421

The Effects of Different Types of Environmental Noise on Academic Performance and Perceived Task Difficulty in Adolescents With ADHD

2015· article· en· W1636120922 on OpenAlexafffund
Lauren Patricia Batho, Rhonda Martinussen, Judith Wiener

Bibliographic record

VenueJournal of Attention Disorders · 2015
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsFluencyReading (process)PsychologyTask (project management)White noiseNoise (video)White (mutation)AudiologyCognitive psychologyDevelopmental psychologyLinguisticsComputer scienceStatisticsMathematics educationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Objective: To examine the effects of environmental noises (speech and white noise) relative to a no noise control condition on the performance and difficulty ratings of youth with ADHD ( N = 52) on academic tasks. Method: Reading performance was measured by an oral retell (reading accuracy) and the time spent reading. Writing performance was measured through the proportion of correct writing sequences (writing accuracy) and the total words written on an essay. Results: Participants in the white noise condition took less time to read the passage and wrote more words on the essay compared with participants in the other conditions, though white noise did not improve academic accuracy. The participants in the babble condition rated the tasks as most difficult. Conclusion: Although white noise appears to improve reading time and writing fluency, the findings suggest that white noise does not improve performance accuracy. Educational implications 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.260
Teacher spread0.247 · 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 teacher head, 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

Citations25
Published2015
Admission routes2
Has abstractyes

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