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Record W2768029893 · doi:10.17269/cjph.108.6048

Ethical challenges in FASD prevention: Scientific uncertainty, stigma, and respect for women’s autonomy

2017· article· en· W2768029893 on OpenAlexafffundvenueabout
Natalie Zizzo, Éric Racine

Bibliographic record

VenueCanadian Journal of Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersKids Brain Health Network
KeywordsStigma (botany)AutonomyPsychologyEthical issuesEngineering ethicsSocial psychologyPolitical sciencePsychiatryEngineeringLaw

Abstract

fetched live from OpenAlex

Fetal alcohol spectrum disorder (FASD) is a leading form of neurodevelopmental delay in Canada, affecting an estimated 3000 babies per year. FASD involves a range of disabilities that entail significant costs to affected individuals, families, and society. Exposure to alcohol in utero is a necessary factor for FASD development, and this has led to FASD being described as "completely preventable". However, there are significant ethical challenges associated with FASD prevention. These challenges revolve around 1) what should be communicated about the risks of alcohol consumption during pregnancy, given some ongoing scientific uncertainty about the effects of prenatal alcohol exposure, and 2) how to communicate these risks, given the potential for stigma against women who give birth to children with FASD as well as against children and adults with FASD. In this paper, we share initial thoughts on how primary care physicians can tackle this complex challenge. First, we recommend honest disclosure of scientific evidence to women and the tailoring of information offered to pregnant women. Second, we propose a contextualized, patient-centred, compassionate approach to ensure that appropriate advice is given to patients in a supportive, non-stigmatizing way.

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)0.010
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.148
GPT teacher head0.369
Teacher spread0.221 · 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 designOther design
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

Citations23
Published2017
Admission routes4
Has abstractyes

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