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Record W2135396860 · doi:10.7202/1069349ar

Fetal Alcohol Spectrum Disorder Standards: Supporting Children in the Care of Children’s Services

2020· article· en· W2135396860 on OpenAlexaffvenueabout
Dorothy Badry

Bibliographic record

VenueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First Peoples · 2020
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderPsychological interventionMedicinePerspective (graphical)PopulationWelfareFoster carePsychologyNursingFamily medicinePsychiatryPediatricsEnvironmental healthPregnancyPolitical science

Abstract

fetched live from OpenAlex

The purpose of this research was to examine the utilization of enhanced practice standards for children in care with Fetal Alcohol Spectrum Disorder (FASD). Children in care with FASD represent a vulnerable population and require multiple supports from a cross-disciplinary perspective. Children removed from the care of their parents were identified as having needs beyond standard care provided within Children’s Services in Alberta. To address this concern a project was initiated in 2002 and completed in 2005 which identified positive benefit from an increase in caseload hours for workers responsible for children with FASD in the Aboriginal Unit including more contact with children and additional supports for foster parents. Standards regarding family visitation are also highlighted. An additional casework position was developed in order to decrease caseloads and meet the standards. Changing the way child welfare and foster care services are delivered for children with FASD is an important phenomenon to study and this research may guide future interventions.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.186
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.291
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First PeoplesSame topicPrenatal Substance Exposure EffectsFrench-language works237,207