A study of Iranian immigrants’ experiences of accessing Canadian health care services: a grounded theory
Bibliographic record
Abstract
BACKGROUND: Immigration is not a new phenomenon but, rather, has deep roots in human history. Documents from every era detail individuals who left their homelands and struggled to reestablish their lives in other countries. The aim of this study was to explore and understand the experience of Iranian immigrants who accessed Canadian health care services. Research with immigrants is useful for learning about strategies that newcomers develop to access health care services. METHODS: The research question guiding this study was, "What are the processes by which Iranian immigrants learn to access health care services in Canada?" To answer the question, a constructivist grounded theory approach was applied. Initially, unstructured interviews were conducted with 17 participants (11 women and six men) who were adults (at least 18 years old) and had immigrated to Canada within the past 15 years. Eight participants took part in a second interview, and four participants took part in a third interview. RESULTS: Using a constructivist grounded theory approach, "tackling the stumbling blocks of access" emerged as the core category. The basic social process (BSP), becoming self-sufficient, was a transitional process and had five stages: becoming a stranger; feeling helpless; navigating/seeking information; employing strategies; and becoming integrated and self-sufficient. We found that "tackling the stumbling blocks of access" was the main struggle throughout this journey. Some of the immigrants were able to overcome these challenges and became proficient in accessing health care services, but others were unable to make the necessary changes and thus stayed in earlier stages/phases of transition, and sometimes returned to their country of origin. CONCLUSION: During the course of this journey a substantive grounded theory was developed that revealed the challenges and issues confronted by this particular group of immigrants. This process explains why some Iranian immigrants are able to access Canadian health care effectively while others cannot. Many elements, including language proficiency, cultural differences, education, previous experiences, financial status, age, knowledge of the host country's health care services, and insider and outsider resources work synergistically in helping immigrants to access health care services effectively and appropriately.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".