A patient co-led project to set care and research priorities for older adults with cancer
Bibliographic record
Abstract
INTRODUCTION: Older adults with cancer face unique challenges, including complex needs and systemic barriers to care. These factors affect their medical treatment decisions and overall quality of life, while caregivers face concurrent burdens. Building on a multi-phase, patient- and community-engaged project, this survey study aimed to validate and rank research and care priorities for older adults with cancer in British Columbia, Canada generated from our earlier work. MATERIALS AND METHODS: A cross-sectional survey was conducted with healthcare professionals, older adults with cancer, caregivers, and community members recruited from health and community organizations. Respondents completed a 19-item online survey with five open-ended questions. Respondents rated and ranked six care and six research priorities identified from our previous work. Quantitative analysis utilized descriptive statistics to assess the significance of priorities, while thematic analysis examined factors influencing respondents' prioritization and decision-making. RESULTS: Data from106 respondents were analyzed. Financial barriers (e.g., medication costs, transportation, housing) was identified as the top care priority, followed by continuity of care. The highest-ranked research priority was implementing geriatric assessment and co-management by interdisciplinary teams. Open-ended responses emphasized financial strain, the need for holistic and culturally competent care, and challenges older adults face navigating fragmented healthcare systems. DISCUSSION: This study highlights the need for integrated geriatric oncology services that address financial, cultural, and systemic barriers. These priorities provide a foundation for developing tailored interventions and policies to improve cancer care for older adults in Canada. Differences in rankings across groups have methodological implications for inclusive, patient and community-engaged 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".