Research Reflections: Advancing linguistic and epistemic equity for sex, gender and diversity in oncology care research: Moving forward and together as a community
Bibliographic record
Abstract
In recent years, equity issues have taken on great importance, particularly in the field of oncology. Indeed, Canada has faced significant challenges, including global migration, limited resources, and climate changes that have exerted undeniable impacts on equity in cancer care services. Furthermore, the COVID-19 pandemic was a significant milestone that exacerbated many pre-existing health inequities. Despite these challenges, oncology nurse researchers shoulder the responsibility to contribute to health and epistemic equity (i.e., creation, usage, and diffusion of knowledge). This responsibility transcends Canada’s official languages (French and English), reflecting Canada’s rich linguistic diversity, with more than 4.6 million individuals (12.7%) primarily using languages other than English or French at home, such as Mandarin, Yue, Cree languages, and many more (Statistics Canada, 2022). Numerous researchers, including those featured in this journal, have taken a proactive stance in raising awareness and advocating for improved oncology research on health and epistemic equity (Varcoe et al., 2015; Winkfield et al., 2020). Alongside scholars and researchers, numerous national and international organizations, including the American Society of Clinical Oncology and the Canadian Partnership Against Cancer, have also expressed a commitment to address this crucial issue.
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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 | 0.104 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.043 |
| Scholarly communication | 0.036 | 0.037 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.024 | 0.045 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".