Knowledge sharing to support newly arrived refugee families’ settlement and children’s educational outcomes: insights for educators
Bibliographic record
Abstract
Forced displacement of people is increasing globally. In this context, sharing of knowledge amongst practitioners involved in refugee settlement can generate new insights into resource availability, quality, innovation, and creation of Communities of Practice. The research discussed in this paper, which adopted a qualitative, participatory methodology, had two aims. First, to gather community hub leaders’ and centre facilitators’ knowledge and practices relating to newly arrived refugee families’ settlement and their children’s educational outcomes in Australia. Second, to translate this knowledge into a Knowledge Translation Framework (KTF) to support practitioners’ work with families and children. Thematic analysis was conducted on interviews with 32 participants from 21 community hubs and centres across Australia, to examine their practices supporting the settlement of refugee families and children’s early learning, and transition to school. Collectively, the findings highlight the importance of a holistic, place-based, soft-entry support grounded in reflective practices, empowering families, and building connections, trust, and relationships with them. Also, they accentuate that refugee support is intertwined with contextual knowledge and factors of geographies, organisational systems, environments, families’ well-being, resources, and practitioners’ ongoing professional development. The implications of these findings and the KTF for various stakeholders, including educators, in enabling newly arrived families’ settlement and promoting children’s educational outcomes are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".