Parenting and child adjustment in immigrant and refugee children: A systematic review
Bibliographic record
Abstract
According to the UN High Commission for Refugees, there are approximately 11 million refugees in the world, and this number keeps increasing. Canada and Australia are among the top three resettlement countries in the world, which is reflected in the fact that 1 in 5 Canadians and 1 in 4 Australians originated from another country. These statistics highlight the prominence of international migration and a greater need to support these populations. This review investigates studies that have examined: 1) immigrant and refugee children's adjustment (emotional problems, behaviour, and academic achievement); 2) parental adjustment (depression, anxiety, or PTSD); and 3) styles of parenting and the parent-child relationship. This is a growing area of research that has not yet been summarized in a systematic review. As part of a Canadian team, in collaboration with an Australian team, we reviewed the research in this area. This review seeks to provide a better understanding of family adjustment within the immigrant and refugee context to inform the development of parent support programs. .Conclusion Our search yielded many articles to examine. Approximately one in ten articles met our criteria, so we have identified a large number of studies addressing the issue of family adjustment. We have identified 153 articles to be analyzed, 34 articles to be used as resources, and 490 articles that may be included in the study but need to be reviewed further. From this large wealth of articles, we will have a thorough understanding of recent research conducted on immigrant and refugee family adjustment. In reviewing the articles, it was essential to have a clear coding manual, as this reduced ambiguity. The importance of having a team was highlighted when resolving questions about coding and clarifying coding rules. The studies included populations from a wide range of countries in South America, the Former Soviet Union, the Middle East, Africa, and Asia. In addition to having a diverse background, immigrants and refugees were also adjusting to life in a variety of host countries from North America, Australia, Europe, and Israel. We found that the majority of the articles were quantitative rather than qualitative and focused on immigrant populations. A very small number of articles examined interventions in this population, indicating an area for further research.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".