Exploring Cancer Patients’ and Caregivers’ Perspectives and Knowledge Regarding Biomarker Testing in Canada
Bibliographic record
Abstract
While biomarker testing can provide various benefits for cancer patient outcomes, numerous challenges persist that cause inequities in access across Canada. An online survey consisting of 51 questions was disseminated to evaluate biomarker testing and precision medicine knowledge and experiences from Canadian patients and caregivers. Responses were recorded between June 2023 and January 2024 and assessed various aspects of the biomarker testing experience including the expectations and challenges of patients. Quantitative and qualitative analyses were conducted using Microsoft Excel and R for descriptive and correlative data analysis, respectively. Among the 74 responses, patients reported an overall moderate experience with positive outcomes for those who underwent biomarker testing, including changes to treatment plans and the shrinking of tumours. The main challenges identified included knowledge gaps, a lack of testing availability, turnaround time for results, and financial constraints, all of which contribute to the disparities in biomarker testing access. Qualitative analysis of responses further emphasized a strong patient desire for patient-centred care and collaborative decision-making for biomarker testing options and treatment planning. Addressing these challenges through increased education, policy advocacy, and advancing infrastructure can help to reduce interprovincial inequities in biomarker testing and contribute to improving cancer patient outcomes.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".