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Record W2990605731 · doi:10.1177/1757975919888452

Promoting the health of children and young people who migrate: reflections from four regional reviews

2019· review· en· W2990605731 on OpenAlexaff
Jill Thompson, Hannah Fairbrother, Grace Spencer, Penny Curtis, Christa Fouché, Karen Hoare, Deirdre Hogan, Jacqui O’Riordan, Bukola Salami, Melody Smith, Bethany Taylor, Victoria Whitakker

Bibliographic record

VenueGlobal Health Promotion · 2019
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Alberta
FundersWorldwide Universities Network
KeywordsHealth promotionPopulationPolitical scienceEconomic growthPsychologyMedicineEnvironmental healthPublic healthNursing

Abstract

fetched live from OpenAlex

Calls to enhance the health of migrant population sub-groups are strengthening, with increasing evidence documenting the relationship between migration and health outcomes. Despite the importance of migration to global health promotion, little research has focused on the health experiences of young migrants. As part of a Worldwide University Network project, we completed four systematic reviews examining the existing evidence base on the health experiences of children and young people who migrate. In this commentary, we share commonalities with the international evidence but also reflect on some of the challenges, omissions and limitations. These insights expose significant gaps and methodological shortcomings in the evidence - providing space for new research that seeks to identify the influences on migrant children's health.

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)0.023
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.147
GPT teacher head0.463
Teacher spread0.316 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2019
Admission routes1
Has abstractyes

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