‘Thank you for helping our children’: refugee families’ perspectives
Bibliographic record
Abstract
Background Many refugee and asylum-seeker families perceive education as a gateway to a more secure future. Building literacy skills in a new language can be a way to help young people feel a greater connection with their new country and increase their chances of smooth integration. However, teachers can lack confidence when working with children and young people from refugee families, and schools are often under-resourced when it comes to meeting the needs of these learners.Purpose A tutoring project was developed for children from a local refugee community in Scotland to support their educational achievement and sense of inclusion in school. This study sought to understand the views and perspectives of the tutored children and families.Methods Qualitative data were collected through questionnaires and a focus group discussion. Eighteen parents and nineteen children shared their experiences. Reflexive thematic analysis was used to identify themes which emerged through both parent and child data.Findings The project appeared to address areas of priority for parents, many of whom emphasised the importance of education. Data suggested a perceived positive impact on the children’s achievement and confidence in school. The children appeared to enjoy and value the tutor sessions. Relationships between tutor and child were seen as fundamental to learning and confidence. The overwhelming response from both the children and their families was one of value, appreciation and a desire for the project to continue.Conclusion Through its engagement with the voices of the children and families involved, this study makes an important contribution to the literature on interventions for children and young people from refugee families and confirms the value of projects that support the educational development and integration of these children and young people. Research that seeks the perspectives of the children, young people and families involved is essential if we are to gather well-rounded and meaningful data on the effectiveness of such interventions.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".