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Record W2799369260 · doi:10.1093/eurpub/cky048.049

1.11-P25Hidden Voices: Reflections on the health experiences of children who migrate

2018· article· en· W2799369260 on OpenAlexaboutno aff
Jill Thompson, Hannah Fairbrother, Penny Curtis

Bibliographic record

VenueEuropean Journal of Public Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySociology

Abstract

fetched live from OpenAlex

Background: UNICEF highlights that across the globe 50 million children have migrated and over half of these have been forcibly displaced due to conflict. The term ‘migrant children’ can encapsulate a range of different groups, including children of economic migrants, documented and undocumented refugees and asylum seekers and unaccompanied asylum seeking and refugee children. For receiving nations, migration can pose significant challenges for health systems, due to the vast array of needs that these heterogeneous groups might have. Methods: The project brings together four regional systematic reviews (Europe, Australasia, Africa and USA/Canada), which all explore the question “What is known about the health experiences and perceptions of children and young people who have migrated?” Papers included in the review had to report data generated directly with children (up to 18 years of age) who had migrated during their own lifetimes. Results: We will highlight the different ways in which children’s migration status is defined and reported within the four regions and reflect on the characteristics of studies included in the reviews. Conclusions: Within each of the four regions we found a general lack of clarity in the literature regarding the reporting of children’s own migration status. Children’s migration status is often conflated with that of their parents. Children’s voices are frequently subsumed within those of their adult parents or carers. Those studies in which children have been actively involved in reporting their health experiences tend to generate quantitative data derived from pre-defined questionnaires. Main messages: Definition, recording and reporting of children’s migration status is inconsistent across the literature. There is a paucity of studies which actively seek to explore migrant children’s health and wellbeing and their individual migration journeys from their own perspectives. Further research into child migrant health would benefit from taking a child-focussed approach.

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)0.029
metaresearch head score (Gemma)0.073
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: none
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0050.007
Scholarly communication0.0100.014
Open science0.0020.009
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0140.002

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 head0.127
GPT teacher head0.401
Teacher spread0.274 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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