A Survey of Older Adults’ Self-Managing Cancer
Bibliographic record
Abstract
BACKGROUND: Older adults living with cancer can experience significant challenges in managing their cancer treatment[s], care, and health. Cancer self-management is much discussed in the research literature, but less is known about the perceptions and experiences of older adults', including their self-management capacities and challenges. This study explored the factors that supported and hindered cancer self-management for older Canadian adults living with cancer. METHODS: We conducted a 17-item population-based telephone survey in the Canadian province of British Columbia among older adults (age ≥ 65) living with cancer. Descriptive and inferential statistics were used to analyze quantitative data and thematic analysis for open-text responses. RESULTS: 129 older adults participated in the study (median age 76, range: 65-93), of which 51% were living with at least one other chronic illness. 20% reported challenges managing their cancer treatment and appointments, while only ~4% reported financial barriers to managing cancer. We organized the findings around enabling and encumbering factors to older adults cancer self-management. The main encumbering factors to self-management included health system and personal factors (physical and emotional challenges + travel). Whereas enablers included: access to interpersonal support, helpful care team, interpersonal support and individual mindset. CONCLUSIONS: Considering factors which enable or encumber older adults' cancer self-management is critical to supporting the growing aging population in the work required to manage cancer treatment and navigate cancer services. Our findings may guide the development of tailored resources for bolstering effectual self-management for older Canadians living with 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".