Use of participatory action research to support Syrian refugee mothers in the resettlement period in Canada: A longitudinal study
Bibliographic record
Abstract
Research has shown that refugees in a foreign country often experience physical and mental health challenges upon resettlement (Ahmad et al., 2021; Salam et al., 2022). In Canada, refugee women experience a range of physical and mental barriers, including poor access to interpreter services and transportation, and a lack of accessible childcare, all of which can negatively affect their successful integration (Stirling Cameron et al., 2022). Social factors that support Syrian refugees to settle successfully in Canada have been unexplored systematically. This study examines these factors from the perspectives of Syrian refugee mothers living in the province of British Columbia (BC). Framed by principles of intersectionality and community-based participatory action research (PAR), the study draws on Syrian mothers' perspectives of social support in early, middle, and later phases of resettlement. A qualitative longitudinal design consisting of a sociodemographic survey, personal diaries, and in-depth interviews was used to gather information. Descriptive data were coded, and theme categories were assigned. Six themes emerged from data analysis: (1) Steps in the Migration Journey; (2) Pathways to Integrated Care; (3) Social Determinants of Refugee Health; (4) COVID-19 Pandemic Impacts and Ongoing Resettlement; (5) Strength-Based Capabilities of Syrian mothers; (6) Peer Research Assistant's Research (PRAs) Experience. Results from themes 5 and 6 are published separately. Data obtained in this study contribute to the development of support services that are culturally appropriate and accessible to refugee women living in BC. Our objectives are to promote the mental health and improve the quality of life of this female population, and to enable it to access healthcare services and resources in a timely manner.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".