Barriers to Healthcare Access for Black Communities in Kingston and London, Ontario
Bibliographic record
Abstract
Healthcare access is recognized as a fundamental human right by the World Health Organization. In Canada, the Canada Health Act mandates equitable and accessible healthcare for all. However, many communities, particularly the Black community, continue to face significant barriers due to socioeconomic factors. These challenges are deeply rooted in systemic racism, socio-economic barriers, and cultural biases, which disproportionately impact the health outcomes of Black individuals. This study explores the specific barriers to healthcare access within the Black community using pre-existing qualitative data. We analyzed semi-structured interviews with 15 Black community members in Kingston and 10 in London, Ontario, using NVivo software to systematically code the data and identify key themes. Additionally, a literature review was conducted, which revealed recurring themes such as lack of culturally competent care, distrust of healthcare institutions, and economic barriers. The analysis of the interview data confirmed these themes and highlighted additional issues related to service type, time and availability, and experiences of racism and discrimination. Despite individual differences, these findings consistently emphasize the pressing need for targeted interventions and policy reforms to address these disparities. However, our study was constrained by the limited availability of race-based data in Canada, underscoring the urgent need for increased research and data collection to advocate for equitable healthcare access and monitor the effectiveness of health services for marginalized communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".