The Cancer Survivorship Journey: Models of Care, Disparities, Barriers, and Future Directions
Bibliographic record
Abstract
Although the number of long-term cancer survivors has increased substantially over past years, the journey of survivorship does not always include high-quality, patient-centered care. A variety of survivorship care models have evolved based on who provides this care, the survivor population, the site of care, and/or the capacity for delivering specific services. Other areas of survivorship care being explored include how long follow-up care is needed, application of a risk-based approach to survivorship care, and the role of the survivor in his or her own recovery. However, there is little evidence indicating whether any models improve clinical or patient-reported outcomes. A newer focus in survivorship care has included assessment of potential disparities; the sociodemographic characteristics of population subgroups associated with barriers to receiving high-quality cancer treatment may also affect the survivorship period. Developing policies and programs to address disparities in survivorship care is not simple, and examining how financial hardship affects cancer outcomes, reducing economic barriers to care, and increasing incorporation of patient-centered strategies may be important components. Here too, there is little evidence regarding the best strategies to address these disparities. Barriers to providing high-quality, patient-centered survivorship care include lack of evidence, lack of a trained survivorship workforce, lack of reimbursement structures/insurance coverage, and lack of a health care system that reduces fragmented care. Future research needs to focus on developing a survivorship care evidence base, exploring strategies to facilitate provision of survivorship care, and disseminating best survivorship care practices to diverse and international audiences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".