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Record W3214092506 · doi:10.1080/09297049.2021.1998407

COVID-19 mental health impact among children with early brain injury and associated conditions

2021· article· en· W3214092506 on OpenAlexaff
Tricia S. Williams, Angela Deotto, Samantha D. Roberts, Meghan K. Ford, Naddley Désiré, Sarah J. Cunningham

Bibliographic record

VenueChild Neuropsychology · 2021
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsHospital for Sick ChildrenYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMental healthSocial isolationPsychologyStressorPsychiatryNeuropsychologyMedicineClinical psychologyCognition

Abstract

fetched live from OpenAlex

2 years, 4 months) with a range of diagnoses (i.e., neonatal stroke, hypoxic ischemic encephalopathy (HIE), congenital heart disease (CHD) and preterm birth (<32 weeks)). The abbreviated CoRonavIrus Health Impact Survey (CRISIS) was completed by parents as part of their child's routine intake for neuropsychological services. Questions included COVID-19 specific ratings of child mental health impact, child, and parent stressors, with open-ended questions regarding negative and positive COVID-19 related changes. Over 40% of parents described moderate to extreme influence of COVID-19 on their child's mental health. Common child stressors reported included restrictions on leaving the home and social isolation. Among parents, the most common stress reported was caring for their child's education and daily activities. Children's mental health impact was associated with social isolation, parent mental health, COVID-19 economic concern, and number of siblings in the home. Child's age, sex, brain injury severity, or intellectual functioning were not associated with reported COVID-19 mental health impact. Some COVID-19 positives were identified, namely increased quality family time. Findings reflect the significant pandemic mental health impact among neurologically at-risk children and their families. Implications to future clinical needs and considerations for neuropsychological practice 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 score0.039
Threshold uncertainty score0.630

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 head0.328
Teacher spread0.316 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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