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Record W4297982311 · doi:10.15173/child.v1i1.3120

Short and Long Term Impacts and Implications of Fetal Alcohol Syndrome Disorders on Cognitive Development in Childhood

2022· article· en· W4297982311 on OpenAlexaboutno aff
Selina Chow, Sophia Farcas, Urwa Ghazi, Emma Huang, Ya Jing Liu

Bibliographic record

VenueThe Child Health Interdisciplinary Literature and Discovery Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionAffect (linguistics)Fetal alcohol syndromeMedicinePandemicPsychiatryStigma (botany)Fetal alcoholAlcohol consumptionPsychologyClinical psychologyPregnancyDevelopmental psychologyDiseaseCoronavirus disease 2019 (COVID-19)Alcohol

Abstract

fetched live from OpenAlex

Fetal alcohol syndrome disorders (FASD) represent a collection of disorders with which a child is born, due to maternal consumption of alcohol during pregnancy. Known as an invisible disability, its prevalence is difficult to capture, in part due to societal stigma and the lack of physical markers contributing to diagnostic difficulty. In Canada, FASD prevalence is estimated to be around 4%. The symptoms experienced by a child with FASD are typically classified into two categories: primary disabilities to describe functional deficits since birth as a result of the impact of alcohol on the brain; and secondary disabilities that occur later in life as a result of a child’s environment and primary disabilities. The impacts of FASD will affect each child differently in both the types and severity of disabilities. A major challenge faced by children and youth with FASD is receiving adequate mental health support, as well as evidence-informed practices involved in improving behavioural and cognitive functioning. COVID-19 has dramatically affected both children with FASD and their caregivers, likely exacerbating existing challenges. With increased rates of alcohol consumption and other mediating factors, experts are concerned about rising FASD rates during the pandemic.

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 score0.217
Threshold uncertainty score0.632

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 studies0.0010.000
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 head0.010
GPT teacher head0.305
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

Citations1
Published2022
Admission routes1
Has abstractyes

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