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Record W4321381023 · doi:10.21742/ijaner.2022.7.1.01

Attention Deficit Hyperactivity Disorder and Dysregulated Eating

2022· article· en· W4321381023 on OpenAlexaff
Janice Arsenault, Kathryn E. Weaver

Bibliographic record

VenueInternational Journal of Advanced Nursing Education and Research · 2022
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsOvereatingImpulsivityContext (archaeology)PsychologyAttention deficit hyperactivity disorderClinical psychologyIntervention (counseling)Eating disordersPsychological interventionBinge eatingPsychiatryDevelopmental psychologyObesityMedicine

Abstract

fetched live from OpenAlex

This study was conducted to identify features of dysregulated eating in the context of children living with Attention Deficit Hyperactivity Disorder (ADHD). A deeper understanding of the association between various eating behaviors and specific moderators unique to ADHD was sought to help healthcare providers identify measurable and predictive features that direct nutritional assessment, support, and interventions. Using the Fineout-Overholt, Melnyk, Stillwell, and Williamson (2010) method, seven studies that included children aged 4 and 15 from the Czech Republic, Greece, Germany, Iran, and Korea were reviewed.Results revealed dysregulated eating patterns involving non-traditional eating schedules, increased eating frequency, episodes of overeating, preference for calorically dense food, high intake of sugary foods and beverages, and diets of higher amounts of processed and fast foods.In conclusion, underlying symptomology characteristics such as impulsivity, inattention, and emotional dysregulation may serve to mediate dysregulated eating behaviors in children living with ADHD.Early recognition and intervention could alter the progression of dysregulated eating behavior and its negative sequelae.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.448
Teacher spread0.402 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Advanced Nursing Education and ResearchSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207