STRATEGIES TO ACCESS HEALTH AND SOCIAL SERVICES FOR ENGLISH-SPEAKING OLDER ADULTS IN QUEBEC: A QUALITATIVE CASE STUDY
Bibliographic record
Abstract
Considering that French is the dominant language in Quebec, that relatively few francophone providers of health and social services are able to speak English, and that English-speaking older adults (OAs) have low levels of bilingualism, anglophone OAs are more likely than their francophone peers to face language barriers when accessing health and social services. However, little is known about the strategies English-speaking OAs put into place to overcome the difficulties encountered due to language barriers when they access these services. We therefore aimed to document the strategies used by English-speaking OAs when, due to language barriers, they faced difficulties in accessing health and social services. We conducted a qualitative case study with ten English-speaking OAs in the Eastern Townships in Quebec. Through interviews and document reviews, we collected data which we then analyzed thematically. We identified seven strategies used by English-speaking OAs: investigating for health- and access-related information in English, creating their own services, entering the health and social services system offered in French, entering the health and social services system with help from others, putting the responsibility of overcoming the language barrier on the provider, splitting that responsibility, and taking on the responsibility. Our results highlight a potential burden associated with the involvement of the English-speaking community in enabling English-speaking OAs to access health and social services.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".