MétaCan
Menu
Back to cohort
Record W3117181099 · doi:10.32370/ia_2020_12_10

Characteristics of Social Work with Children with Disabilities in Great Britain

2020· article· en· W3117181099 on OpenAlexvenueno aff
Iryna Rizak

Bibliographic record

VenueIntellectual Archive · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSocial workLegal guardianSocial WelfareWork (physics)PsychologyPolitical science

Abstract

fetched live from OpenAlex

The article describes the features of social work for children with disabilities in the UK. Defined the concept of children with disabilities. Described the interpretation of the concept of children with disabilities according to the medical and social model. Presented the main structures that can provide social services in the UK to children with disabilities. Analyzed the peculiarities of British social work with children with disabilities in the activities of the Centers for Social Services. Determined the main types of social services provided by such centers are described. The concept of independent living and activities of the centers independent living are described. The types of services provided to children with disabilities and their families (information; counseling; legal; housekeeping services, home support, leisure organization, guardianship services) are identified. The programs implemented at the state level to provide support are given and providing social assistance to children with disabilities and their families. Pointed out the orientation of state programs and their provision of social services to improve the educational outcomes of children with disabilities. The main directions of social work for children with disabilities in the United Kingdom are described. Characterized medical and social work (basic care) and social work with children and families.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.043
GPT teacher head0.348
Teacher spread0.304 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIntellectual ArchiveSame topicGlobal Health Workforce IssuesFrench-language works237,207