Lived Experiences of Iranian Immigrants with Long Covid in Canada: An Interpretive Description Approach
Bibliographic record
Abstract
Long COVID (LC) remains an evolving global health challenge, presenting persistent and varied symptoms that particularly affect marginalized populations. This thesis examines the lived experiences of Iranian immigrants in Canada suffering from LC, exploring how they navigate the condition and what their healthcare experiences reveal about systemic challenges. To gain insight into these experiences, I recruited twenty Iranian immigrants with LC through purposive and snowball sampling via Telegram groups. I utilized an Interpretive Description (ID) methodology, employing semi-structured interviews that provided a detailed look into participants’ daily struggles. A thematic analysis revealed several recurring issues. Through a rigorous analytical process, I found five themes: embodied disruptions and symptom unpredictability, systemic barriers and healthcare navigation, emotional and existential distress, economic and workplace insecurity, cultural integration and healing pathways. Findings underscore systemic healthcare shortcomings, emphasizing the urgent need for more culturally attuned, interdisciplinary approaches. Language barriers and cultural stigma emerged as additional obstacles, yet many participants demonstrated resilience, drawing on community ties and personal coping strategies. This research contributes to the growing body of knowledge on LC by amplifying the voices of Iranian immigrants. It underscores the need for culturally competent, interdisciplinary approaches to care and provides actionable insights for policymakers, healthcare providers, and immigrant health services. By foregrounding lived experience, the study advances understanding of LC within immigrant context and advocates for a more equitable healthcare system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".