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Record W2345928599 · doi:10.4137/sart.s34545

Prevention of Fetal Alcohol Spectrum Disorder: Current Canadian Efforts and Analysis of Gaps

2016· article· en· W2345928599 on OpenAlexaffabout
Nancy Poole, Rose A. Schmidt, Courtney Green, Natalie Hemsing

Bibliographic record

VenueSubstance Abuse Research and Treatment · 2016
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsThe Society of Obstetricians and Gynaecologists of CanadaBritish Columbia Centre of Excellence for Women's Health
Fundersnot available
KeywordsDelphi methodPublic healthFetal Alcohol Spectrum DisorderService providerPsychologyMedicineEnvironmental healthHealth promotionPublic relationsBusinessNursingService (business)PregnancyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

Effective prevention of risky alcohol use in pregnancy involves much more than providing information about the risk of potential birth defects and developmental disabilities in children. To categorize the breadth of possible initiatives, Canadian experts have identified a four-part framework for fetal alcohol spectrum disorder (FASD) prevention: Level 1, public awareness and broad health promotion; Level 2, conversations about alcohol with women of childbearing age and their partners; Level 3, specialized support for pregnant women; and Level 4, postpartum support for new mothers. In order to describe the level of services across Canada, 50 Canadian service providers, civil servants, and researchers working in the area of FASD prevention were involved in an online Delphi survey process to create a snapshot of current FASD prevention efforts, identify gaps, and provide ideas on how to close these gaps to improve FASD prevention. Promising Canadian practices and key areas for future action are described. Overall, Canadian FASD prevention programming reflects evidence-based practices; however, there are many opportunities to improve scope and availability of these initiatives.

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 score0.251
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.342
Teacher spread0.306 · 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

Citations47
Published2016
Admission routes2
Has abstractyes

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