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Record W3100252681 · doi:10.1108/add-07-2020-0014

An invisible problem: stigma and FASD diagnosis in the health and justice professions

2020· article· en· W3100252681 on OpenAlexaffabout
Katharine Dunbar Winsor

Bibliographic record

VenueAdvances in Dual Diagnosis · 2020
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsConcordia University
Fundersnot available
KeywordsStigma (botany)OriginalityInvisibilityFetal Alcohol Spectrum DisorderPsychologyCriminal justiceEconomic JusticeHealth carePsychiatryMedicineSocial psychologyCriminologyPolitical science

Abstract

fetched live from OpenAlex

Purpose Fetal alcohol spectrum disorder (FASD) is a complex lifelong disorder impacting the brain and body. Individuals with FASD may require lifelong supports and are at a higher risk of experiencing adverse outcomes, including incarceration. Individuals with FASD face stigma related to FASD that impacts disclosure of the diagnosis and access to supports. The prevalence of FASD exceeds that of other developmental disabilities, yet it remains less visible and stigmatized. Design/methodology/approach Interviews conducted with health-care and justice professionals in a Canadian province explore their experiences attending to FASD and challenges of stigma. Findings While difficult to access, diagnosis provides a pathway to supports and is crucial in the criminal justice process. Visibility and invisibility in health and justice systems highlight the lack of understanding of FASD and surrounding stigma. When unaddressed, individuals with FASD face additional challenges stemming from a lack of information and strategies by professionals involved in their lives. Originality/value Stigma represents a significant and complex issue intertwined in understandings of FASD. This research explores this relationship and the mechanisms through which FASD stigma has impacts in health-care and justice settings.

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)0.001
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 score0.064
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.346
Teacher spread0.318 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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