Barriers to Equity in Cancer Survivorship Care: Perspectives of Cancer Survivors and System Stakeholders
Bibliographic record
Abstract
As more cancer patients survive into post-treatment, the challenge of managing their survivorship care is confronting health care systems globally. In striving to deliver high quality survivorship care, equity constitutes a particularly troublesome challenge. We analyzed accounts from both cancer survivors and stakeholders within care system management to uncover insights with respect to barriers to equitable cancer survivorship services. Beyond the social determinants of health that shape inequities across all of our systems, the cancer care system involves a pattern of prioritizing biomedicine, evidence-based options, and care standardization. We learned that these lead to system rigidities that not only compromise the individualization essential to person-centered care but also obscure the attention to group differences that becomes indispensable to responsiveness to inequities. On the basis of these insights, we reflect on what may be required to begin to redress the current and projected inequities with respect to access to appropriate cancer survivorship supports and services.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".