Management of Cancer and Health After the Clinic Visit: A Call to Action for Self-Management in Cancer Care
Bibliographic record
Abstract
Individuals with cancer and their families assume responsibility for management of cancer as an acute and chronic disease. Yet, cancer lags other chronic diseases in its provision of proactive self-management support in routine, everyday care leaving this population vulnerable to worse health status, long-term disability, and poorer survival. Enabling cancer patients to manage the medical and emotional consequences and lifestyle and work changes due to cancer and treatment is essential to optimizing health and recovery across the continuum of cancer. In this paper, the Global Partners on Self-Management in Cancer puts forth six priority areas for action: Action 1: Prepare patients and survivors for active involvement in care; Action 2: Shift the care culture to support patients as partners in cocreating health and embed self-management support in everyday health-care provider practices and in care pathways; Action 3: Prepare the workforce in the knowledge and skills necessary to enable patients in effective self-management and reach consensus on core curricula; Action 4: Establish and reach consensus on a patient-reported outcome system for measuring the effects of self-management support and performance accountability; Action 5: Advance the evidence and stimulate research on self-management and self-management support in cancer populations; Action 6: Expand reach and access to self-management support programs across care sectors and tailored to diversity of need and stimulation of research to advance knowledge. It is time for a revolution to better integrate self-management support as part of high-quality, person-centered support and precision medicine in cancer care to optimize health outcomes, accelerate recovery, and possibly improve survival.
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 | 0.000 | 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) | 0.000 | 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".