Toward equitably high-quality cancer survivorship care
Bibliographic record
Abstract
Although models of cancer survivorship care are rapidly evolving, there is increasing evidence of health disparities among cancer survivors. In the current context, Canada's survivorship care systems privilege some and not others to receive high-quality care and optimize their health outcomes. The aim of this study was to improve survivorship care systems by helping clinicians and decision makers to a better understanding of how various psychosocial/political factors, survivors' health experiences and health management strategies might shape the development of and access to high-quality survivorship care for Canadians with cancer. Using a nursing epistemological approach informed by critical and intersectional perspectives, we conducted a three-phased Interpretive Description study. We engaged in critical textual analysis of documentary sources, a secondary analysis of interview transcripts from an existing database, and qualitative interviews with 34 survivors and 12 system stakeholders. On the basis of these data, we identified individual, group, and system factors that contributed to gaps between survivors' expected and actual survivorship care experiences. By understanding what shapes survivorship care systems and resources, we help illuminate and unravel the complex nature of the issue, supporting clinicians and decision makers to find multi-layered approaches for equitably high-quality survivorship care.
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.031 | 0.029 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".