Impact of the COVID-19 pandemic on Muslim older immigrants in Edmonton, Alberta: A community-based participatory research project with a local mosque
Bibliographic record
Abstract
OBJECTIVE: Older Muslim immigrants experience multiple vulnerabilities living in Canada. This study explores the experiences of Muslim older adults during the COVID-19 pandemic to identify ways to build community resilience as part of a community-based participatory research partnership with a mosque in Edmonton, Alberta. METHODS: Using a mixed-methods approach, check-in surveys (n = 88) followed by semi-structured interviews (n = 16) were conducted to assess the impact of COVID-19 on older adults from the mosque congregation. Quantitative findings were reported through descriptive statistics, and thematic analysis guided the identification of key findings from the interviews using the socio-ecological model. RESULTS: Three major themes were identified in consultation with a Muslim community advisory committee: (a) triple jeopardy leading to loneliness, (b) decreased access to resources for connectivity, and (c) organizational struggles to provide support during the pandemic. The findings from the survey and interviews highlight various supports that were missing during the pandemic for this population. CONCLUSION: The COVID-19 pandemic exacerbated the challenges associated with aging in the Muslim population and contributed to further marginalization, with mosques being sites of support during times of crises. Policymakers and service providers must explore ways of engaging mosque-based support systems in meeting the needs of older Muslim adults during pandemics.
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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 | 0.006 | 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 | 0.016 | 0.005 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".