Difficulties accessing health care services during the COVID-19 pandemic in Canada: examining the intersectionality between immigrant status and visible minority status
Bibliographic record
Abstract
BACKGROUND: Difficulties accessing health care services can result in delaying in seeking and obtaining treatment. Although these difficulties are disproportionately experienced among vulnerable groups, we know very little about how the intersectionality of realities experienced by immigrants and visible minorities can impact their access to health care services since the pandemic. METHODS: Using Statistics Canada's Crowdsourcing Data: Impacts of COVID-19 on Canadians-Experiences of Discrimination, we combine two variables (i.e., immigrant status and visible minority status) to create a new variable called visible minority immigrant status. This multiplicative approach is commonly used in intersectionality research, which allows us to explore disadvantages experienced by minorities with multiplicative identities. RESULTS: Main results show that, compared to white native-born, visible minority immigrants are less likely to report difficulties accessing non-emergency surgical care (OR = 0.55, p < 0.001), non-emergency diagnostic test (OR = 0.74, p < 0.01), dental care (OR = 0.71, p < 0.001), mental health care (OR = 0.77, p < 0.05), and making an appointment for rehabilitative care (OR = 0.56, p < 0.001) but more likely to report difficulties accessing emergency services/urgent care (OR = 1.46, p < 0.05). CONCLUSION: We conclude that there is a dynamic interplay of factors operating at multiple levels to shape the impact of COVID-19 related needs to be addressed through changes in social policies, which can tackle unique struggles faced by visible minority immigrants.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".