Self-reported health status and associated factors among community-dwelling Afro-Caribbean Black Canadians: a cross-sectional comparison before and during the pandemic
Bibliographic record
Abstract
BACKGROUND: Social Determinants of Health are linked to health outcomes. Racial discrimination can lead to marginalization, poor quality of care, and racial inequities in health outcomes. The COVID-19 pandemic exacerbated pre-existing health, social, and economic disparities within marginalized communities. However, data focusing on Canadian Black populations are limited. This study aims to identify how the pandemic impacted the health of Black communities and to identify associated factors, both prior to and during the pandemic, to inform areas for action and service planning. METHODS: This community-based cross-sectional study included English- or French-speaking Canadians self-identifying as African, Caribbean, or Black, in the Greater Toronto Area. The survey collected data from November 2022 to May 2023 on sociodemographic and clinical factors, and perceived health status. Data were based on retrospective self-reports. Such self-reported data are relevant to participatory research methods that require participatory data. Perceived general and mental health status, overall and by characteristics, was described. Key factors associated with perceived health before and during the pandemic were explored using multivariable logistic analysis. RESULTS: 388 individuals, aged 1–74 years, were included, mostly English-speaking and non-Canadian-born. Respectively 81% and 76% indicated their general and mental health as good or excellent before the pandemic, compared to 49% and 45% during the pandemic. University education (OR = 3.25), physical activity (OR = 2.87) and multimorbidity (OR = 0.15) were significantly associated with self-reported general health before the pandemic, while more factors, including immigration status, age, employment status, and insurance coverage, contributed to mental health perception. During the pandemic, women reported good/excellent general health or mental health 50–60% less often than men. Multimorbidity, immigration status, and household income were also associated with general health. French-speaking Black Canadians reported a lower proportion of good/excellent mental health, but those privately insured or physically active reported 3 times higher proportions of good or excellent mental health. CONCLUSIONS: This study provides needed health status information from a community-based sample of Black Canadians prior to and during the COVID-19 pandemic and explores how social determinants of health vary in different contexts. A comparison before and during the pandemic and community involvement strengthened this cross-sectional study.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".