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Record W1936891538 · doi:10.1111/cfs.12214

Trial and error: attending to language barriers in child welfare service provision from the perspective of frontline workers

2015· article· en· W1936891538 on OpenAlexafffund
Sarah Maiter, Ramona Alaggia, Adrienne S. Chan, Bruce Leslie

Bibliographic record

VenueChild & Family Social Work · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsChildren's Aid SocietyUniversity of the Fraser ValleyUniversity of TorontoYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterpreterFocus groupConfidentialityWorkloadPerspective (graphical)PsychologyRigourWelfareLanguage barrierService (business)Public relationsApplied psychologyMedical educationSocial psychologyMedicineBusinessComputer sciencePolitical scienceComputer securityMarketing

Abstract

fetched live from OpenAlex

Abstract This paper reports on a qualitative study that used focus groups to gather data from the perspective of child protection workers on their experiences in providing services to clients with limited English proficiency ( LEP ). The goals of the study were to understand processes of relationship building and service provision that could contribute to improved practice. Focus groups are ideal in obtaining data on how a group deals with particular situations and for understanding their decision‐making processes. Measures to ensure rigour and trustworthiness were taken throughout the study. Findings provide rich and multilayered insights into the complexity of providing services for families with LEP showing that agencies use bilingual workers as well as interpreters for services. Participants noted, however, that services continued to be inconsistent with unclear guidelines and training about how and when to access interpreters. Lack of training for bilingual workers was also identified. Confidentiality issues, barriers to engaging with clients, problems with the quality of interpretation and role confusion together with benefits including interpreters being cultural and language ‘conduits’ and links to culturally specific services; enhanced communication decreasing the possibility of inaccurate assessments and lessening power differentials; and help with workload were noted.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.408
Teacher spread0.354 · 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 designQualitative
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
Published2015
Admission routes2
Has abstractyes

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