reVue Canadienne de sOins infirmiers en OnCOlOgie Health disparities in cancer care: Foundational concepts
Bibliographic record
Abstract
It is exciting to see nurses in cancer care increasing their attention to health equity and social justice. Such concerns have not dominated research and practice in relation to cancer; perhaps, in part, because cancer carries less social stigma than health issues where equity concerns are foregrounded such as mental health problems, violence or HIV (with exceptions such as lung cancer in people who smoke). Cancer nurses are amplifying their attention to health inequities (or disparities) at a time when understanding of the foundational concepts is becoming increasingly robust. The purpose of this intro-duction is to review the concepts foundational to integrating health inequities in health research and practice and to con-sider their specific relevance to cancer care. The terms ‘disparities ’ and ‘inequities ’ are often used inter-changeably and both must be distinguished from inequalities. ‘Inequality ’ is a broad term referring to differences between
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".