Bridging the gap: Patient and healthcare professional perspectives on accessing and using cancer genetics services by racialized communities
Bibliographic record
Abstract
Introduction: Racial and ethnic disparities exist across the entire cancer genetics service pathway. Racialized individuals are less likely to be referred for genetic testing and more likely to receive inconclusive results due to underrepresentation in genomic databases. This limits access to early detection and prevention, high-risk cancer screening, and genetic testing for atrisk relatives, ultimately impacting health outcomes. Limited efforts in Canada to identify racial disparities impedes the development and implementation of appropriate interventions.Aim: To explore the barriers and enablers to accessing and using cancer genetics services by racialized communities. Methods: Semi-structured interviews were conducted with racialized patients and healthcare professionals involved in cancer genetics care across Ontario. Analysis followed an interpretive description approach. Results: 11 patients and 10 healthcare professionals (six genetic counselors, two primary care physicians, one medical geneticist, and one oncologist) were interviewed. Four themes emerged shaping the experiences of racialized communities with cancer genetics services: (1) medical mistrust, (2) structural barriers, (3) family history gaps, and (4) the need for more inclusive care. Both groups described how historical and personal experiences of medical harm and discrimination diminish patient trust and shape their willingness to engage with genetics services. Participants also discussed how the healthcare system’s structure creates multiple barriers to accessing cancer genetics care. Specifically, they noted workforce shortages among primary care physicians and genetics specialists, long wait times for genetic counseling appointments and testing, clinical time constraints, and limited genetics knowledge among patients and referring providers. Participants discussed gaps in family health history, stemming from cultural norms and privacy around illness, geographic separation between relatives, and limited access to health records; reliance on family history for testing eligibility can unintentionally disadvantage racialized communities. Lastly, participants emphasized the need for greater inclusivity in the healthcare system, including workforce diversity, language accessibility, and cultural awareness among healthcare professionals. Conclusion: Multi-level, intersectional barriers exist to accessing cancer genetics services for racialized patients. This work will be used to co-develop interventions to optimize access and reduce disparities.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".