Digital learning preferences of Arabic-speaking older immigrants in Canada: A qualitative case study
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the importance of digital inclusion for equitable and healthy aging. Older immigrants experience unique needs and challenges in using information and communication technologies compared to other older adults. Despite the proliferation of digital learning programs for older adults, there is minimal evidence of digital literacy learning needs and strategies relevant to older immigrants. The aim of this study is to explore learning approaches and digital engagement amongst Arabic-speaking older immigrants. This community-based qualitative descriptive study used co-designed group digital learning sessions. Two organizations supporting local ethnocultural communities in a municipality in Alberta, Canada recruited 31 older immigrants who spoke Arabic, Farsi, and Kurdish. Data collection included semi-structured interviews, focus groups, and observations of digital learning sessions. A total of seventeen learning sessions were completed with nineteen participants each attending five to six sessions. Findings highlight the iterative nature of the program sessions, the importance of catering to participants’ interests, the relevance of peer support, and language, sensory and digital variability barriers to learning. Digital literacy programs for immigrant older adults should adjust for language learning needs, maintain a flexible approach, tailor lessons to individual needs, foster social support, and address external factors such as limited digital access and transportation barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".