SARS-CoV-2 testing and COVID-19 related primary care use among people with citizenship, permanent residency, and temporary immigration status in British Columbia: Cross-sectional analysis of population-based administrative data
Bibliographic record
Abstract
Abstract Background Having temporary immigration status affords limited rights, workplace protections, and access to services. There is not yet research data on impacts of the COVID-19 pandemic for people with temporary immigration status in Canada. Methods We use linked administrative data to describe SARS-CoV-2 testing, positive tests, and COVID-19 primary care service use in British Columbia from January 1, 2020, to July 31, 2021, stratified by immigration status (Citizen, Permanent Resident, Temporary Resident). We plot the rate of people tested and the rate of people confirmed positive for COVID-19 by week from April 19, 2020, to July 31, 2021, across immigration groups. Results 4.9% of people with temporary immigration status had a positive test for SARS-CoV-2 over this period, compared to 4.0% among people with permanent residency and 2.1% among people who hold Canadian citizenship. This pattern is persistent by sex/gender, age group, neighborhood income quintile, health authority, and in both metropolitan and small urban settings. At the same time we observe lower access to testing and COVID-19 related primary care among people with temporary status. Interpretation People with temporary immigration status in BC experience higher SARS-CoV-2 test positivity; alarmingly, this was coupled with lower access to testing and primary care. Interwoven immigration, health and occupational policies place people with temporary status in circumstances of precarity and higher health risk. Extending permanent residency status to all immigrants residing in Canada and decoupling access to health care from immigration status could reduce precarity due to temporary immigration status.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".