Challenges and barriers to services for immigrant seniors in Canada: “you are among others but you feel alone”
Bibliographic record
Abstract
Purpose This paper seeks to explore varied interrelated challenges and barriers experienced by immigrant seniors. Design/methodology/approach Senior immigrants representing diverse ethnicities (Chinese, Afro Caribbean, Former Yugoslavian, Spanish) described their challenges, support needs, and barriers to service access. Service providers and policy makers from organizations serving immigrant seniors were interviewed to elicit their views on barriers to access and appropriateness of services for immigrant seniors. Qualitative methods were employed to enhance understanding of meanings, perceptions, beliefs, values, and behaviors of immigrant seniors, and investigate sensitive issues experienced by vulnerable groups. The qualitative data were subjected to thematic content analysis. Findings Seniors reported financial and language difficulties, health problems, discrimination, family conflicts, and social isolation. Although most immigrant seniors appreciated the standard of living in Canada and the services provided to seniors, most believed that support received was inadequate. Seniors encountered systemic (e.g. government policies), institutional (e.g. culturally inappropriate programs), and personal (e.g. transportation, language problems) barriers to accessing social and health services. Service providers and policy makers faced high costs of programs, inadequate financial and human resources, inadequate information about needs of immigrant seniors, inadequate geographical coverage, and lack of inter‐sectoral collaboration. Practical implications The challenges experienced by immigrant seniors have implications for programs and policies and can inform the development of culturally sensitive and appropriate services. Social implications The barriers encountered by service providers in assisting immigrant seniors point to the importance of inter‐sectoral coordination, cultural sensitivity training, and expansion of service providers' mandates. Originality/value This study revealed numerous unmet needs for successful acculturation of immigrant and refugee seniors in Canada. It also reveals that the most cogent and sustainable approach to close this chasm of support deficits, unattended challenges, and complex stressors is to implement a model that simultaneously addresses the three levels and use a multisectoral approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".