A Mobile Breast Cancer Survivorship Care App: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Cancer survivors living in rural areas experience unique challenges due to additional burdens, such as travel and limited access to specialists. Rural survivors of breast cancer have reported poorer outcomes, poorer mental health and physical functioning, and lower-than-average quality of life compared to urban survivors. OBJECTIVE: To explore the feasibility and acceptability of developing a mobile health survivorship care app to facilitate care coordination; support medical, psychosocial, and practical needs; and improve survivors' long-term health outcomes. METHODS: An interactive prototype app, SmartSurvivor, was developed that included recommended survivorship care plan components. The prototype's feasibility and acceptability were tested by a sample of breast cancer survivors (n=6), primary care providers (n=4), and an oncologist (n=1). RESULTS: Overall, both survivors and providers felt that SmartSurvivor was a potentially valuable tool to support long-term survivorship care plan objectives. Portability, accessibility, and having one place for all contact, treatment, symptom tracking, and medication summaries was highly valued. CONCLUSIONS: Our pilot study indicates that SmartSurvivor is a feasible and acceptable approach to meeting survivorship care objectives and the needs of both breast cancer survivors and their health care providers. Exploration of mobile health options for supporting survivorship care plan needs is a promising area of research.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".