Exploring levels and factors associated with transition challenges for Syrian refugee parents resettled in Canada
Bibliographic record
Abstract
Although the literature has documented numerous challenges Syrian refugees face during their resettlement in Canada, the unique transition experiences of Syrian refugee parents remain underexplored. This study examines demographic, community and social, migration, and health-related factors that influence the level of difficulty experienced by Syrian refugee parents in Canada during their transition. This cross-sectional, interview-based study was conducted from March 2021 to March 2022, involving 540 Syrian refugee parents in Ontario with at least one child under the age of 18. Transition difficulty was measured based on the question "How difficult has the transition into Canada been for you?" Responses ranged from 1 representing "Not difficult at all" and 5 representing "Very difficult". 6.5% of participants rated their transition as "Not difficult at all", 15.9% as "Not difficult, "20.6% as "Neutral", 43% as "Difficult", and 13.7% as "Very difficult". Results of the multiple linear regression analyses indicated that greater transition difficulty was significantly associated with experiences of discrimination at children's school events (Adjβ = 0.138, p = 0.038), dissatisfaction with friendships (Adjβ = 0.134, p = 0.006), being over age 45 (Adjβ = 0.301, p = 0.047), lower proficiency in English or French (Adjβ = - 0.145, p = 0.008), longer duration spent in Canada (Adjβ = 0.123, p < 0.001), Blended Visa Office-Referred program (Adjβ = 0.530, p = 0.026) and poorer mental health (Adjβ = 0.173, p < 0.001). The findings from this study highlight the need for policies and frameworks aimed at improving resettlement efforts for refugee parents, thereby promoting the overall well-being of Syrian refugee families in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".