The case of Iranian immigrants in the greater Toronto area: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Iranians comprise an immigrant group that has a very different cultural background from that of the mainstream Canadian population and speaks a language other than English or French; in this case mainly Farsi (Persian). Although Iranian immigrants in Toronto receive a high proportion of care from Farsi-speaking family physicians and health care providers than physicians who cannot speak Farsi, they are still not satisfied with the provided services. The purpose of this study was to identify the obstacles and issues Iranian immigrants faced in accessing health care services as seen through the eyes of Iranian health care professionals/providers and social workers working in Greater Toronto Area, Canada. METHODS: Narrative inquiry was used to capture and understand the obstacles this immigrant population faces when accessing health care services, through the lens of fifty Iranian health care professionals/providers and social workers. Thirty three health care professionals and five social workers were interviewed. To capture the essence of issues, individual interviews were followed by three focus groups consisting of three health care professionals and one social worker in each group. RESULTS: Three major themes emerged from the study: language barrier and the lack of knowledge of Canadian health care services/systems; lack of trust in Canadian health care services due to financial limitations and fear of disclosure; and somatization and needs for psychological supports. CONCLUSION: Iranians may not be satisfied with the Canadian health care services due to a lack of knowledge of the system, as well as cultural differences when seeking care, such as fear of disclosure, discrimination, and mistrust of primary care. To attain equitable, adequate, and effective access to health care services, immigrants need to be educated and informed about the Canadian health care system and services it provides. It would be of great benefit to this population to hold workshops on health topics, and mental health issues, build strong ties with the community by increasing their involvement, use plain language, design informative and health related websites in both Farsi and English, and provide a Farsi speaking telephone help line to answer their health related issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".