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Record W3169920788 · doi:10.1016/j.jmh.2021.100054

African immigrant child health: A scoping review

2021· review· en· W3169920788 on OpenAlexaff
Christa Fouché, Solina Richter, Helen Vallianatos, Alleson Mason, Higinio Fernández‐Sánchez, Valentina Mazzucato, Michael Kariwo, Bukola Salami

Bibliographic record

VenueJournal of Migration and Health · 2021
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Alberta
FundersWorldwide Universities Network
KeywordsSomaliMental healthImmigrationScope (computer science)PopulationMedicineEnvironmental healthGerontologyEconomic growthGeographyPsychiatry

Abstract

fetched live from OpenAlex

The health of migrant children is a pressing issue. While most African migration takes place within Africa, a significant number of African migrants travel to outside of the continent. This article reports findings from a scoping review on the health of African immigrant children from sub-Saharan Africa now living outside of Africa. A systematic search for studies published between 2000 and 2019 resulted in only 20 studies reporting on the health of children up to 18 years of age migrating from sub-Saharan Africa. Data from these articles were thematically analyzed, highlighting concerns related to the children's nutrition status (n = 8), mental health (n = 7), and physical health (n = 5). Study participants were primarily from Somali and Ethiopia, and most studies were conducted in Australia or Israel. The review highlights several gaps related to the scope, range, and nature of evidence on the health of African immigrant children living outside of Africa. In particular, most focus on children's nutritional and mental health, but pay little attention to other health concerns this specific population may encounter or to the benefits associated with effective responses.

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)0.006
metaresearch head score (Gemma)0.023
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 score0.012
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.143
GPT teacher head0.482
Teacher spread0.339 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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