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Record W3080135072 · doi:10.1017/9781108602105.019

Family-School Relationships in Supporting Refugee Children’s School Trajectories

2020· book-chapter· en· W3080135072 on OpenAlexaff
Mina Fazel, Aoife O’Higgins

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeFeelingPsychologyPsychological interventionMental healthPopulationDevelopmental psychologyPublic relationsSocial psychologyPolitical scienceMedicinePsychotherapistEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Schools can play a key role in helping refugee families manage the transition into the host society. When considering how best to support refugee children, schools should first consider how accessible they are to refugee families in the community and what tools and skills they may need to carry out a holistic assessment given the myriad complexities that this population can present with. Schools should maintain a family lens as they are likely to be well placed to facilitate refugee families feeling a sense of belonging in their host country as well as signposting them to other services if any additional needs become apparent. Refugee children would benefit from schools carrying out comprehensive assessments of their learning needs and cognitive abilities in order to optimise provision and support. Mental health interventions for refugee children have been studied in schools and include assisting parenting in a new environment as well as supporting everyday living skills.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.038
GPT teacher head0.259
Teacher spread0.220 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooks→Same topicMigration, Health and Trauma→French-language works237,207→