Experiences and Perspectives of Caregivers of Francophone Older Adults Accessing Community Health Services in Toronto: an Exploratory Qualitative Study
Bibliographic record
Abstract
Background: Canada is a bilingual country; however, outside of Quebec, health-care services are predominantly offered in English. With the increasing older adult population and stretched health-care resources, Francophone older adults may face significant challenges in accessing care due to their linguistic minority status. This study explores the experiences of caregivers of Francophone older adults in the Greater Toronto Area (GTA). Methods: Using a convenience sampling strategy, caregivers of patients who had undergone geriatric assessment at the Centre Francophone du Grand Toronto (CFGT) were recruited. Participants underwent 45-minute, semi-structured interviews and completed demographic questionnaires. Three independent reviewers conducted qualitative content analysis of the interview transcripts, using the socioecological model of health and NVivo12. Results: Nine participants were primarily female (n=5), with age ranges of 40-49 (n=2), 50-59 (n=3), and 60+ (n=4). They originated from North America (n=5), Africa (n=3), and the Middle East (n=1); about half preferred English over French. Thematic analysis identified three key themes: 1) Barriers Accessing Health Care in the French Language; 2) The Need for Interpreter Support; 3) Importance of Comprehensive Francophone Community Services. Conclusions: Despite the presence of organizations (e.g., CFGT), this study reveals a significant gap in French-language services for older adults in the GTA, leading to increased challenges for their caregivers. Due to linguistic barriers, caregivers must act as interpreters to mitigate the risks associated with miscommunication and potentially worse health outcomes. Addressing these issues requires increasing bilingual health-care providers, enhancing funding for Francophone community services, and improving support systems (e.g., interpreters).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".