Supporting People and Their Caregivers Living with Advanced Cancer: From Individual Experience to a National Interdisciplinary Program
Bibliographic record
Abstract
OBJECTIVES: To discuss the unmet needs of patients living with advanced cancer and their caregivers and to review strategies, including collaborating with community and non-profit organizations, to help improve the experience of living with, and beyond, advanced cancer. DATA SOURCES: Published articles, first person experience (SB), community organization input, and survey data (Canadian Cancer Society). CONCLUSION: People living with advanced cancer face significant challenges, including persistent physical symptoms and psychosocial concerns, difficulties with coordination of care, and possible lack of available resources and supports if the person is no longer being followed by cancer health care professionals. More research is required to better understand the needs of patients and their caregivers living with advanced cancer. Existing resources and supports may be inadequate for this population, and delineation of the unique needs of this population may lead to tailored care plans and, ultimately, an improved experience for patients and caregivers alike. IMPLICATIONS FOR NURSING PRACTICE: Oncology nurses are ideally suited to care for this population to help elucidate their unique unmet needs and collaborate with patients and other clinicians to develop interventions to address such unmet needs. Oncology nurses can liaise with community organizations to identify sources of support and resources for patients and their loved ones and advocate for improved care for patients affected by advanced cancer.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".