Growing inequities by immigration group among older adults: Population-based analysis of access to primary care and return to in-person visits during the COVID-19 pandemic in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background The onset of the COVID-19 pandemic drove a rapid and widespread shift to virtual care, followed by a gradual return to in-person visits. Virtual visits may offer more convenient access to care for some, but others may experience challenges accessing care virtually, and some medical needs must be met in-person. Experiences of the shift to virtual care and benefits of in-person care may vary by immigration experience (immigration status and duration), official language level, and age. We examined use of virtual care and return to in-person visits in the Canadian province of British Columbia (BC), comparing patterns by age and across immigration groups, including length of time in Canada and language level (English) at time of arrival. Methods We used linked administrative health and immigration data to examine total primary care visits (virtual or in-person) and return to in-person visits during the COVID-19 pandemic (2019/20-2021/2) in BC. We examined the proportion of people with any primary care visits and with any in-person visit within each year as measures of access to primary care. We estimated the odds of any primary care visit and any in-person visit by immigration group and official language level assessed prior to arrival: non-immigrants, long-term immigrants, recent immigrants (<5 years) with high assessed English level and recent immigrants (<5 years) with low assessed English level, stratified by age. Results In general, changes in access to primary care (odds of any visit and odds of any in-person visit) were similar across immigration groups over the study period. However, we observed substantial disparities in access to primary care by immigration group among people aged 60+, particularly in recent immigrants with low official language level (0.42, 0.40-0.45). These disparities grew wider over the course of the pandemic. Conclusion Though among younger adults changes in access to primary care between 2019-2021 were similar across immigration groups, we observed significant and growing inequities among older adults, with particularly limited access among adults who immigrated recently and with low assessed English level. Targeted interventions to ensure acceptable, accessible care for older immigrants are needed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".