Service-policy gaps in the settlement journey of Arabic-speaking immigrant newcomer and refugee older adults in Edmonton, Canada
Bibliographic record
Abstract
Immigrant newcomers and refugees (INRs) are two migrant categories that experience consistent systemic barriers to settlement and integration in Canada as older adults. This paper explores the challenges experienced by Arabic-speaking INR older adults in Edmonton, Canada, during settlement and discusses policy and service implications. A qualitative description study using community-based participatory research principles was implemented to evaluate and support digital literacy in Arabic-speaking INR older adults. We included men and women aged 55 and older who identified as immigrants or refugees and spoke Arabic. Experiences of settlement challenges were consistently identified during data collection and engagement of INR older adult participants. A thematic sub-analysis of interviews with (10 individuals and one couple) of participants' narratives was completed in 2022 and was used to identify themes related to settlement barriers for this population. Two main themes were identified: (1) Limited English skills and digital literacy gaps create service barriers for INR older adults, and (2) Gaps in services and policies as basic needs remain unmet. We describe key challenges experienced by INR older adults, such as language barriers, precarious finances, poor access to health care services and lack of transportation and employment opportunities, which hinder successful integration into the new society. This study showcases the ongoing challenges with early settlement and integration that continue despite Canada's well-developed immigration settlement landscape. INR older adults often remain invisible in policy, and understanding their experiences is a first step to addressing their needs for resources that support healthy aging in the post-migration context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 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".