Factors supporting settlement among Syrian refugee women: A longitudinal participatory action research study
Bibliographic record
Abstract
Introduction: Over 13 million Syrians have been forcibly displaced since the start of the Syrian civil war in 2011. In response to this humanitarian crisis, several high-income countries have settled thousands of Syrian refugees. In Canada, over 50,000 Syrian refugees have resettled through varying resettlement programs. Half of the refugees are women who are mothers or of child-bearing age, and who experience numerous health disparities. This article reports findings from a larger, Canadian-based study inquiring into the factors supporting and shaping the settlement and integration experiences among women who are Syrian refugees and mothering. Methods: A longitudinal intersectionality-framed participatory action approach was initiated through multiple meetings with a diverse range of non-profit community organizations focused on refugee health and settlement. Through these meetings, sustainable relationships were formed, and trust was built toward further engaging with the Syrian refugee mothering women population. A core group of 4 women were employed as peer research assistants and were integrated across research processes. Results: In total, 40 Syrian refugee mothering women participated in this study. Six themes emerged from data analysis of their lived experiences of resettlement. Four of these themes are published elsewhere. We focus this article on two of the six key findings: harnessing strength-based capabilities, and peer research assistant experiences. Conclusions: The two findings described in this article convey facilitators that add to understanding influences on the mental well-being of Syrian refugee mothering women. Unique to this study is the novel integration of peer research assistants and a model of support which contributes to an ethical and inclusive approach to understanding lived experiences among refugee women. This article highlights how this model benefits the peer research assistant and promotes community engagement among women.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".