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Record W1976176240 · doi:10.7895/ijadr.v2i3.116

Screening in treatment programs for Fetal Alcohol Spectrum Disorders that could affect therapeutic progress

2013· article· en· W1976176240 on OpenAlexvenueno aff
Therese M. Grant, Natalie Novick Brown, J. Christopher Graham, Nancy Whitney, Dan Dubovsky, Lonnie A. Nelson

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Observational studyIntervention (counseling)PsychologyFetal alcohol syndromeMedicineClinical psychologyAlcohol abuseAlcohol use disorderAlcoholPsychiatryFetal alcoholInternal medicine

Abstract

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Grant, T., Novick Brown, N., Graham, J., Whitney, N., Dubovsky, D. , & Nelson, L. (2013). Screening in treatment programs for Fetal Alcohol Spectrum Disorders that could affect therapeutic progress. The International Journal Of Alcohol And Drug Research, 2(3), 37-49. doi:10.7895/ijadr.v2i3.116 (http://dx.doi.org/10.7895/ijadr.v2i3.116)Aims: While structured intake interviews are the standard of care in substance abuse treatment programs, these interviews often do not screen for cognitive impairments, such as those found in fetal alcohol spectrum disorders (FASD) and other brain-based developmental disorders. The research reported here supports a brief interview protocol, the Life History Screen (LHS), that screens clients unobtrusively for adverse life-course outcomes typically found in FASD, so as to guide follow-up assessments and treatment planning.Design: Two-group observational study.Setting: A three-year case management intervention program in Washington State for high-risk women who abuse alcohol and/or drugs during pregnancy.Participants: Group 1: No prenatal alcohol exposure (N = 463); Group 2: Diagnosed with FASD (Fetal Alcohol Syndrome, Alcohol Related Neurodevelopmental Disorder, fetal alcohol effects, or static encephalopathy) by a qualified physician (N = 25), or suspected of having FASD (reported prenatal alcohol exposure and displayed behaviors consistent with a clinical diagnosis of FASD) (N = 61).Measures: The Addiction Severity Index (ASI) was administered to participants at intake. We analyzed eleven ASI items that corresponded to questions on the LHS in order to assess the potential of the LHS for identifying adults with possible FASD. The Life History Screen itself was not administered.Findings: Analysis of group differences between the diagnosed FASD and suspected FASD groups supported our decision to collapse the two groups for the main analysis. The Life History Screen shows promise as an efficient pre-treatment screen, in that core items are significantly associated with FASD group membership on factors involving childhood history, maternal drinking, education, substance use, employment, and psychiatric symptomatology.Conclusions: The Life History Screen may have utility as a self-report measure that can be used at the outset of treatment to identify clients with cognitive impairments and learning disabilities due to prenatal alcohol exposure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.090
GPT teacher head0.399
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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