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Record W4413636205 · doi:10.1016/j.hmedic.2025.100352

Persistent crisis of fetal alcohol spectrum disorder in South Africa: Time for decisive action

2025· article· en· W4413636205 on OpenAlexaff
Babatope O. Adebiyi, Ferdinand C. Mukumbang

Bibliographic record

VenueMedical Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderAction (physics)Fetal alcoholAlcoholPsychiatryPolitical scienceCriminologyMedicinePsychologyPregnancyBiologyPhysicsGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Fetal Alcohol Spectrum Disorder (FASD) represents a critical public health challenge in South Africa (SA), where the prevalence is among the highest worldwide. This commentary highlights the urgent need for coordinated, multifaceted approaches to address FASD, with prevalence rates as high as 310 per 1,000 individuals, particularly in rural communities. Factors contributing to this high prevalence include historical practices like the "dop system," pervasive poverty, limited healthcare access, and social norms around alcohol use. The consequences of FASD are profound, leading to lifelong impairments and significant economic impacts. Alcohol-related harms cost SA around 65–104 billion Rand annually. Despite the magnitude of the issue, SA lacks a comprehensive national strategy, resulting in fragmented services and care gaps. To mitigate this crisis, a combination of prevention, early diagnosis, and community-based interventions is essential. Suggested strategies include public awareness campaigns, integration of FASD prevention into primary healthcare, enhanced diagnostic services, and community empowerment initiatives. A national response involving government agencies, healthcare providers, educational institutions, community organizations, and private sector stakeholders is imperative. With sustained commitment, South Africa can substantially reduce the burden of FASD, ensuring a healthier future for its communities.

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)0.004
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 score0.183
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.016
GPT teacher head0.293
Teacher spread0.276 · 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
Published2025
Admission routes1
Has abstractyes

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