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Record W4405702758 · doi:10.1177/02724316241311131

Parental Factors Moderate the Association Between COVID-19 Disruption and Adolescent Emotional and Behavioural Difficulties

2024· article· en· W4405702758 on OpenAlexafffund
Kate Van Kessel, Charlotte Aitken, Elizabeth S. Nilsen

Bibliographic record

VenueThe Journal of Early Adolescence · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAssociation (psychology)PsychologyMental healthCoronavirus disease 2019 (COVID-19)Developmental psychologyPerceptionClinical psychology2019-20 coronavirus outbreakMedicinePsychiatryDisease

Abstract

fetched live from OpenAlex

Past work shows that COVID-19 impacted adolescent mental health, but the moderating role of parental factors remains unclear. Ninety-one parent-adolescent dyads (ages 12-15) completed online surveys. Parents reported on COVID-19 disruption within their household, their mental health, parental reflective functioning (i.e., ability to consider the mental state of their child), and their adolescent's emotional and behavioural difficulties. Adolescents rated their own emotional and behavioural difficulties and perception of parental support. Positive associations between household COVID-19 disruption and adolescent difficulties emerged, regardless of informant. However, parental factors linked to adolescent difficulties varied by informant. Parental reflective functioning moderated the association between COVID-19 disruption and adolescent difficulties (parent-report). COVID-19 disruption showed some stronger associations with adolescent difficulties than other parental stress measures, but not consistently. Findings replicate and extend prior work, emphasizing the negative association between COVID-19 disruption and adolescent mental health, while highlighting parental reflective functioning's potential for mitigating adolescent difficulties.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.087
GPT teacher head0.382
Teacher spread0.295 · 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 routes2
Has abstractyes

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