“We Are Like Any Other People, but We Don’t Cry Much Because Nobody Listens”: The Need to Strengthen Aging Policies and Service Provision for Minorities in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study explores the aging experiences and needs of immigrant Muslim communities in an urban center in Alberta, Canada. Over one million Muslims live in Canada, with the majority being immigrants and visible minorities. Aging-focused policies and services have yet to address the needs of this population as larger cohorts begin to enter older age. RESEARCH DESIGN AND METHODS: A community-based participatory research approach was adopted with a community advisory committee co-leading all aspects of the research process. Sixty-seven older adults and stakeholders from diverse ethnocultural immigrant Muslim communities participated in either individual interviews or one of the seven focus groups (2017-2018). Data were transcribed verbatim and thematically analyzed with a focus on factors that support or hinder positive aging experiences in this population. RESULTS: Participants not only described the benefits of growing old in Canada but also identified unique challenges stemming from their social positioning as religious minorities, immigrants, and older adults. We highlight these experiences in three themes: (a) aging while living across places, (b) negotiating access to aging-supportive resources in a time of scarcity, and (c) re-envisioning Islamic approaches to eldercare. DISCUSSION AND IMPLICATIONS: Immigrant Muslim communities report inequities experienced by older community members. There is a need for an in-depth analysis of the ways aging and migration policies intersect to influence the resources that immigrant minorities have access to as they grow old in Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".