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Record W2604682316 · doi:10.1093/pch/pxx003

Supporting the developmental health of refugee children and youth

2017· article· en· W2604682316 on OpenAlexafffundabout
Ripudaman Minhas, Hamish Graham, Thivia Jegathesan, Joelene Huber, Elizabeth Young, Tony Barozzino

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSt. Michael's Hospital
FundersHealth Canada
KeywordsRefugeeGovernment (linguistics)ChecklistMnemonicMental healthMedicinePsychologyDevelopmental psychologyNursingPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

The Canadian Government has announced that over 50,000 refugees from the Middle East will be resettled in Canada by 2018. More than one-third of these refugees are expected to be children. The Canadian Paediatric Society has called for the Canadian government to prepare for the influx of these children. This should include addressing developmental, behavioural, and mental health needs. The focus of this paper is the role of paediatricians and family physicians in caring for the developmental health of refugee children, as a means of supporting their developmental and learning potential. The authors suggest the use of EMPOWER (Education, Migration, Parents and Family, Outlook, Words, Experience of Trauma and Resources), a mnemonic checklist they developed for assessing developmental risk factors in refugee children. EMPOWER can be used along with online web resources such as Caring For Kids New to Canada in providing evidence-informed care to these children.

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)0.007
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: Commentary · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.345
Teacher spread0.321 · 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
GenreCommentary

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

Citations45
Published2017
Admission routes3
Has abstractyes

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