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Record W4313557322 · doi:10.1108/add-04-2022-0014

Hearing the voice of child welfare social workers: planning safe care for a child with or suspected of having fetal alcohol spectrum disorders (FASDs)

2023· article· en· W4313557322 on OpenAlexaff
William Christopher Curran, Matthew C. Danbrook

Bibliographic record

VenueAdvances in Dual Diagnosis · 2023
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsSocial workMedicineStatutory lawPsychological interventionSocial WelfarePsychologyPsychiatryNursingClinical psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Purpose In the early 1970s, clinical evidence emerged documenting causal links between prenatal alcohol exposure (PAE) and children’s behaviors as observed by child welfare social workers (CWSWs). Unfortunately, fetal alcohol spectrum disorders (FASD) remain on the margins of public health priorities. The purpose of this study was to elicit the views of child welfare social workers when responding to case of or suspected FASD. Design/methodology/approach A sample ( N = 18) of CWSWs, allied health professionals and foster parents were interviewed. Findings Findings indicate that social workers struggle with their statutory duty to plan safe care for children with or suspected of having FASD. Emergent themes include struggling with advocacy, professional devaluation and lack of procedural guidance. Practical implications Social workers need a clear pathway and FASD knowledge to guide their interventions and enhance their capacity to advocate for affected children. Originality/value An abundance of research documents the direct effect of PAE on physical, cognitive and behavioral outcomes. However, few studies focus on the critical interface of children with an FASD entering public care and the social workers responsible for planning their safe care. This study sought to document social workers’ response to this vulnerable cohort of children.

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)0.000
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 score0.017
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.304
Teacher spread0.287 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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