Bibliographic record
Abstract
Evidence of health disparities among cancer survivors is growing. Globally, survivorship models of care are evolving rapidly, yet few consider health and social disparities in their development, limiting access to high quality survivorship care for many. In the current context, Canada’s survivorship care systems may privilege some, and not others, to receive high quality survivorship care and optimize health in this context. Understanding the role of disparities in models of care development and access is essential to ensure individual need, rather than social privilege, guides opportunities for high quality survivorship care. This study aims to improve survivorship care systems by helping clinicians and decision makers to a better understanding of how various factors (e.g. social, political, economic, personal) and survivors’ health experiences and health management strategies might shape the development of and access to high quality survivorship care for Canadians with cancer. A nursing disciplinary epistemology, underpinned by pragmatism and informed by critical and intersectional perspectives, served as a framework to explore complexity within survivorship care. A phased qualitative Interpretive Description approach was used to analyze data from three distinct data sources: 1) critical textual analysis of 70+ document sources (e.g., survivorship guidelines, education programs, policies, resources); 2) secondary analysis of multiple transcripts from 19 survivors in an existing data base; and 3) 34 survivor and 12 stakeholder interviews. Survivors described a gap between their expected and actual survivorship care experiences. This gap was shaped by contextual and structural factors that further marginalized some individuals/groups. Factors shaping this gap at all levels included: individual (e.g., previous experiences, social determinants of health, advanced cancer, age); group (e.g., defining standardized “norms”); and system (e.g. efficiency drivers, underdeveloped guidelines, exclusionary messaging such as “cancer can be beaten”). Recommendations arising from these findings ranged from strategies to build survivor trust to integrated policies across social and health sectors to promote survivors’ holistic health. This multilayered, intersectoral approach to understanding what shapes survivorship care systems and resources highlights and unravels the complex nature of the issue, helping clinicians and decisions makers find multi-layered approaches for equitably high quality survivorship care.
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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".