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Record W2982629614 · doi:10.3390/ijerph16214229

Fetal Alcohol Spectrum Disorder: What does Public Awareness Tell Us about Prevention Programming?

2019· article· en· W2982629614 on OpenAlexafffundabout
Peter Choate, Dorothy Badry, Bruce MacLaurin, Kehinde Ariyo, Dorsa Sobhani

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of CalgaryUniversity of TorontoMount Royal University
FundersPolicyWise for Children and Families
KeywordsSpouseHarmAbstinencePublic healthPregnancyMedicinePsychologyPsychiatryNursingPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The prevalence of Fetal Alcohol Spectrum Disorder (FASD) does not appear to be diminishing over time. Indeed, recent data suggests that the disorder may be more prevalent than previously thought. A variety of public education programs developed over the last 20 years have promoted alcohol abstention during pregnancy, yet FASD remains a serious public health concern. This paper reports on a secondary data analysis of public awareness in one Canadian province looking at possible creative pathways to consider for future prevention efforts. The data indicates that the focus on women of childbearing age continues to make sense. The data also suggests that targeting formal (health care providers for examples) and informal support (partner, spouse, family, and friends) might also be valuable. They are seen as sources of encouragement, so ensuring they understand the risks, as well as effective ways to encourage abstinence or harm reduction, may be beneficial for both the woman and the pregnancy. Educating people who might support a woman in pregnancy may be as important as programs targeted towards women who may become or are pregnant. The data also suggests that there is already a significant level of awareness of FASD, thus highlighting the need to explore the effectiveness and value of current prevention approaches.

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)0.003
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 score0.391
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.371
Teacher spread0.333 · 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

Citations14
Published2019
Admission routes3
Has abstractyes

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