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Record W2129863661 · doi:10.3109/17549507.2013.862858

Cost of speech-language interventions for children and youth with foetal alcohol spectrum disorder in Canada

2013· review· en· W2129863661 on OpenAlexafffundabout
Svetlana Popova, Shannon Lange, Larry Burd, Kevin D. Shield, Jürgen Rehm

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2013
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersPublic Health AgencyPublic Health Agency of CanadaUniversity of Washington
KeywordsPsychological interventionFetal Alcohol Spectrum DisorderIntervention (counseling)MedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

This study, which is part of a large economic project on the overall burden and cost associated with Foetal Alcohol Spectrum Disorder (FASD) in Canada, estimated the cost of 1:1 speech-language interventions among children and youth with FASD for Canada in 2011. The number of children and youth with FASD and speech-language disorder(s) (SLD), the distribution of the level of severity, and the number of hours needed to treat were estimated using data from the available literature. 1:1 speech-language interventions were computed using the average cost per hour for speech-language pathologists. It was estimated that ˜ 37,928 children and youth with FASD had SLD in Canada in 2011. Using the most conservative approach, the annual cost of 1:1 speech-language interventions among children and youth with FASD is substantial, ranging from $72.5 million to $144.1 million Canadian dollars. Speech-language pathologists should be aware of the disproportionate number of children and youth with FASD who have SLD and the need for early identification to improve access to early intervention. Early identification and access to high quality services may have a role in decreasing the risk of developing the secondary disabilities and in reducing the economic burden of FASD on society.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.337
Teacher spread0.310 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations21
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Speech-Language PathologySame topicPrenatal Substance Exposure EffectsFrench-language works237,207