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Record W2604079107 · doi:10.3138/jcfs.42.4.599

Needs of Refugee Children in Canada: What can Roma Refugee Families Tell Us?

2011· article· en· W2604079107 on OpenAlexvenueaboutno aff
Christina A Walsh, David Este, Brigette Krieg, Bianca Giurgiu

Bibliographic record

VenueJournal of Comparative Family Studies · 2011
Typearticle
Languageen
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeFocus groupEconomic growthQualitative researchService providerSociologyService (business)MedicinePolitical scienceBusinessSocial science

Abstract

fetched live from OpenAlex

Little attention has been paid to the needs of refugee children in Canada. The last decade has seen increasing numbers of Roma refugees settling in southern Ontario, and this qualitative study explored the needs of Roma refugee children in the education, health and social services sectors. We interviewed or conducted focus groups with 24 Roma and 62 service providers from those sectors. Participants indicated that Roma children had high needs for service in all these sectors, but that families were often unable or unwilling to access services effectively. Specifically, children needed schooling for language acquisition and for the development of social relationships; absenteeism was a concern. Income assistance was needed by Roma and other newly-arriving families. Continuity and quality of health care was an issue. In all sectors, barriers were related to language, culture and the unique history of the Roma people. This paper contextualizes study findings by briefly summarizing Roma historical and current experience in Europe; this experience is critical to an understanding of the difficulties that Roma encounter with institutional systems.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0180.005
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.150
GPT teacher head0.395
Teacher spread0.245 · 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 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

Citations30
Published2011
Admission routes2
Has abstractyes

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