Virtual care models for cancer survivorship
Bibliographic record
Abstract
Abstract Virtual care models for cancer survivorship are needed to support patients living with the chronic effects of cancer treatment, while increasing health system capacity. Characteristics that may be critical to their success have not been adequately studied. This scoping review summarizes previous efforts to virtualize survivorship care to inform future innovations in the field. Four databases were searched for articles published before January 2020, and 24 articles that met selection criteria were included in this analysis. Rationale for pursuing virtual models of care shared two common objectives: (1) the need for sustainable survivorship care, and (2) the opportunity to improve survivorship outcomes. Breast cancer (N = 10) and prostate cancer (N = 4) were the most targeted cancers for virtual survivorship care. The implemented technologies included web platforms (N = 15), telephone calls (N = 12), and smartphone or tablet applications (N = 5). A variety of healthcare professionals were effectively involved in the provision of virtual care. Future virtual care models may benefit from integrating with existing health systems and services, repurposing common technologies, involving allied health professionals, and engaging patients and caregivers from diverse communities in the design of virtual services.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".