Navigating healthcare during the pandemic: Experiences of racialized immigrants and racialized non-immigrants in Ontario's Peel Region
Bibliographic record
Abstract
While public health policies implemented during COVID-19, such as prioritizing essential health services and "no visitor" strategies, were important to treat COVID-19 patients and curb disease outbreaks, their potential negative effects on the health of the general population is a growing concern. Research highlights that these policy changes contributed to a near-universal decline in access to all healthcare services and triggered increased morbidity and mortality rates. However, little is known about how health policy changes differentially shaped healthcare access within and between population groups and regions. Few studies qualitatively examine the indirect effects of policy changes on healthcare access among groups disproportionately impacted by COVID-19. This article examines how COVID-19 health policy changes impacted racialized immigrant and racialized non-immigrants' ability to connect with a provider, navigate telehealth and in-person healthcare, and access specialized healthcare in the Peel Region of Ontario, Canada. Using a Client Centered Framework, findings from in-depth interviews (n = 79) reveal that policy changes generated new (in)abilities for individuals to perceive, seek, reach, pay and engage in healthcare services. Health policy changes created new barriers to reach healthcare, compounding health challenges. While telehealth opened more effective avenues to access healthcare among some people, it created new disparities for individuals with limited English language skills and/or for those experiencing technological inequities. Although individuals recognized their need for specialized healthcare, the prioritization of essential services, gaps in health insurance coverage, and new COVID-19 economic inequities created barriers to specialized healthcare. We close with a discussion of the impacts for policy and practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".