Ongoing impacts of the COVID-19 pandemic on access to primary care among im/migrant communities in British Columbia, Canada
Bibliographic record
Abstract
The COVID-19 pandemic changed healthcare delivery in multiple ways, including a widespread shift to virtual care. Evidence of how these changes were experienced is mixed and varies among populations and Canadian provinces. We sought to generate new information about how these changes were experienced by im/migrants in British Columbia (BC), assessing their impact on access to virtual and in-person primary care. We conducted in-depth, semi-structured interviews in Dari, English, Farsi, Spanish, and Tigrinya with im/migrants living in BC for less than 10 years. We analyzed 50 interviews using a team-based approach to reflexive thematic analysis to explore how changes in service delivery in the context of the COVID-19 pandemic impacted im/migrant's healthcare experiences and access in BC. Interview participants described impacts of changes in service delivery in terms of accessibility, human connections, quality of care, and safety. Impacts were experienced positively as opportunities or negatively as obstacles. Experiences were shaped by immigration status, English language fluency, having a regular source of primary care before the pandemic, and economic resources. An overarching theme was trust, with healthcare experiences during the pandemic either increasing or decreasing participants’ trust in the healthcare system. Our findings reveal that within im/migrant communities, the same changes in health service delivery were experienced differently, depending on various determining factors. Whether people experienced opportunities or obstacles, and increased or decreased trust, was shaped by modifiable policies that predate the pandemic and will persist beyond the pandemic unless significant and intentional, evidence-based changes are implemented. • The same changes in healthcare were experienced differently among im/migrants interviewed. • Some experienced opportunities for accessibility, quality of care, human connection, and safety, others experienced obstacles. • Security of status, language supports, and access to regular primary care shaped experiences. • Virtual care can provide opportunities for improved access among im/migrants, when appropriately supported . • It is important to be attentive and proactive in rebuilding trust in the 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".