Optimizing Social Support in Oncology with Digital Platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increased cancer prevalence and survival rates coupled with earlier patient discharges from hospitals have created a greater need for social support. Cancer care is both short term and long term, requiring acute treatments, treatments for remission, and long-term screenings and treatment regimens. Health care systems are already overwhelmed and often struggle to provide social support systems for everyone. Caregivers are limited in number, and even when they are available, they often lack necessary information, skills, or resources to meet the needs of patients with cancer. The act of caregiving presents various challenges, and caregivers themselves often need social support as well. Despite these needs, most social support programs are targeted toward patients alone. Given the prevalence of cancer and known needs of these patients and their caregivers, the ability to identify those who need social support is crucial. Further, the scalability and overall availability of social support programs is vital for successful patient care. This paper establishes the benefits of social support for both patients and caregivers coping with cancer treatments, explores innovative ways of identifying patients who may need social support using digital tools, and reviews potential advantages of digital social support programs.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it