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Record W4404024945 · doi:10.22215/cujs.v3i1.4929

Examining ADHD Children's Learning Behaviours: The Impact of Parental Self-Efficacy and Relationship Quality Amid COVID-19

2024· article· en· W4404024945 on OpenAlexaff
Zara Hewson, Marina Parvanova, Maria Rogers

Bibliographic record

VenueCarleton undergraduate journal of science. · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsCarleton University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PsychologyQuality (philosophy)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Developmental psychology2019-20 coronavirus outbreakClinical psychologyMedicineVirologyInternal medicineOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

This study aimed to explore differences in learning behaviours between children with and without ADHD during the COVID-19 pandemic, considering parental self-efficacy and parent-child relationship quality (e.g., closeness and conflict). Data was used from a nationwide survey conducted in Spring 2021, encompassing 468 parents of school-aged children (aged 3-18 years), comparing 278 parents of children with ADHD (Mage = 9.88) to 190 parents of typically developing children (Mage= 9.45). The study found that children of parents who reported higher self-efficacy and parent-child relationship closeness exhibited higher/more positive learning behaviours, while children of parents who reported higher parent-child relationship conflict demonstrated lower/poorer learning behaviours. Only in children with ADHD, learning behaviours were lower when parents and children demonstrated closer relationships. Additionally, parent-child relationship conflict partially mediated the link between parental self-efficacy and learning behaviours in children with ADHD. These findings highlight parents' struggles in supporting children with ADHD during COVID-19.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.108
GPT teacher head0.415
Teacher spread0.307 · 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
Published2024
Admission routes1
Has abstractyes

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