Toward an Understanding of Cancer as an Issue of Social Justice: Perspectives and Implications for Oncology Nursing
Bibliographic record
Abstract
Within the fields of oncology practice and research, cancer has historically been and continues to be understood as primarily biologically produced and physiologically driven. This understanding is rooted in biomedicine, the dominant model of health and illness in the Western world. Yet, there is increasing evidence of inequities in cancer that are influenced by social and structural inequities. In this article, we propose that cancer-related inequities ought to be seen as issues of social justice, and, given nursing's longstanding commitments to social justice, they ought to be a priority for oncology nurses. Using a social justice lens, we highlight potential social injustices in the form of inequities in cancer outcomes and access to cancer care across the cancer continuum. Our intention is not to provide an exhaustive review of evidence, but to provide our perspective, adding to the dialogue surrounding health equity and cancer while shifting the narrative away from an understanding of cancer inequities as stemming from "lifestyle" and "behavioural" choices. We conclude by exploring the implications of considering cancer inequities as social injustices for nursing practice.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| 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".