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Record W2028008456 · doi:10.1007/s10566-013-9226-x

Canadian Children and Youth in Care: The Cost of Fetal Alcohol Spectrum Disorder

2013· article· en· W2028008456 on OpenAlexafffundabout
Svetlana Popova, Shannon Lange, Larry Burd, Jürgen Rehm

Bibliographic record

VenueChild & Youth Care Forum · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsCentre for Addiction and Mental Health
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsFetal Alcohol Spectrum DisorderPrenatal alcohol exposureMedicineEnvironmental healthPopulationDemographyWelfareEpidemiologyIncidence (geometry)PediatricsPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: A high prevalence of prenatal alcohol exposure has been reported among children in care and thus, the risk of fetal alcohol spectrum disorder (FASD) in this population is high. OBJECTIVE: The purpose of the current study was to estimate the number of children (0-18 years) in care with FASD and to determine the associated cost by age group, gender, and province/territory in Canada in 2011. METHODS: The prevalence of children in care by province/territory was obtained from the Canadian Child Welfare Research Portal, and the number of children in care with FASD for each province/territory was estimated from available epidemiological studies. In order to calculate the total cost per province/territory, the cost per individual per day, by age group, was applied to the respective number of children in care with FASD. RESULTS: The estimated number of children in care with FASD ranged from 2,225 to 7,620, with an annual cost of care ranging from $57.9 to $198.3 million Canadian dollars (CND). The highest overall cost ($29.5 to $101.1 million CND) was for 11-15 year-olds. CONCLUSION: The study findings can be used to demonstrate the substantial economic burden that FASD places on the child welfare system. Attention towards the needs of this population and prevention efforts to reduce FASD incidence in Canada, and other countries are urgently needed.

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 score0.201
Threshold uncertainty score0.756

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 head0.005
GPT teacher head0.203
Teacher spread0.199 · 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

Citations50
Published2013
Admission routes3
Has abstractyes

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