Follow-Up Care for Breast and Colorectal Cancer Across the Globe: Survey Findings From 27 Countries
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe follow-up care for breast and colorectal cancer survivors in countries with varying levels of resources and highlight challenges regarding posttreatment survivorship care. METHODS: We surveyed one key stakeholder from each of 27 countries with expertise in survivorship care on questions including the components/structure of follow-up care, delivery of treatment summaries and survivorship care plans, and involvement of primary care in survivorship. Descriptive analyses were performed to characterize results across countries and variations between the WHO income categories (low, middle, high). We also performed a qualitative content analysis of narratives related to survivorship care challenges to identify major themes. RESULTS: Seven low- or /lower-middle-income countries (LIC/LMIC), seven upper-middle-income countries (UMIC), and 13 high-income countries (HICs) were included in this study. Results indicate that 44.4% of countries with a National Cancer Control Plan currently address survivorship care. Additional findings indicate that HICs use guidelines more often than those in LICs/LMICs and UMICs. There was great variation among countries regardless of income level. Common challenges include issues with workforce, communication and care coordination, distance/transportation issues, psychosocial support, and lack of focus on follow-up care. CONCLUSION: This information can guide researchers, providers, and policy makers in efforts to improve the quality of survivorship care on a national and global basis. As the number of cancer survivors increases globally, countries will need to prioritize their long-term needs. Future efforts should focus on efforts to bridge oncology and primary care, building international partnerships, and implementation of guidelines.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".