Improving equity and wellness in cancer care with people of Latin American and African Descent: a study protocol
Bibliographic record
Abstract
Background: Cancer inequities such as late access to cancer screening and diagnosis affect people of African and Latin American descent in Canada. These inequities in addition to experiences of racism and discrimination and unequal living and working conditions are detrimental to their wellness. We aim to delineate together with people of African and Latin American descent a patient-oriented pathway to improve their equity and wellness in cancer care. Methods: This is a 3-year community-based and patient-oriented participatory research study. The study will take place in Alberta and Ontario and will involve 125 participants including people with cancer, family and community members of African and Latin American descent, and health care providers. We will conduct in-depth interviews with patients and families and focus groups with community members. Together with patient partners and community collaborators, we will delineate a patient-oriented pathway in cancer care to improve equity and wellness for people of African and Latin American descent in Canada. Finally, we will explore the acceptability of the pathway with a small sample of patients, families and health care providers. Conclusion: This study will advance our knowledge of equity and wellness in people with advanced cancer from racialized communities in Canada; and increase our understanding of how racialized populations live through a cancer diagnosis. The study will also generate knowledge of how a patient-oriented health equity pathway can contribute to reduce cancer inequities in the care of our study populations.
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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 | 0.078 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".