Facilitators supporting settlement among women who are Syrian refugees and mothering: Findings from a longitudinal participatory action research study
Bibliographic record
Abstract
Abstract Background Over 13 million Syrians have been forcibly displaced since the start of the Syrian civil war in 2011. In response to this overwhelming 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 these refugees are women who are mothers or of child-bearing age. Women who are refugees and mothering comprise a population that experience numerous health disparities including higher rates of depression and anxiety, social exclusion and lower socioeconomic status. 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 This study employed a longitudinal intersectionality-framed participatory action approach. This design was initiated through multiple community-based meetings with a diverse range of non-profit organizations focused on refugee health and settlement. Through these meaningful engagements, sustainable relationships were formed and trust was built toward further immersing ourselves within the Syrian refugee mothering women population. Consequential establishment of a core group of 4 women led to the emergence of a peer research assistant model which informed data collection, reflexive analysis, and knowledge mobilization activities. Results In total, 40 Syrian refugee mothering women participated in this study and six themes emerged from data analysis of their lived experiences of resettlement. Four of the 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 While multiple barriers continue to exist within recent re-settlement processes of Syrian refugee mothering women, these two findings convey facilitators that add to understanding influences to the mental well-being of this often overlooked population. 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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".