1.11-P25Hidden Voices: Reflections on the health experiences of children who migrate
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".