“What We Want Is More Access…”: Experiences of Supportive Cancer Care and Strategies for Advancement in a Canadian Provincial Cancer Care Organization
Bibliographic record
Abstract
OBJECTIVES: Despite calls for better supportive care, patients and families still commonly bear significant responsibility for managing the physical and mental health and social challenges of being diagnosed with and treated for cancer. As such, there is increased advocacy for integrated supportive care to ease the burden of this responsibility. The purpose of this study was to understand patient and caregiver experiences with supportive care to advance its delivery at a large provincial cancer care organization in Canada. METHOD: We used a qualitative descriptive approach to analyze focus groups with patients and caregivers from seven sites across the large provincial cancer care organization. RESULTS: = 12). Participants highlighted positive and negative aspects of their experience and strategies for improvement. These are depicted in three themes: (1) improving patient and provider awareness of services; (2) increasing access; (3) enhancing coordination and integration. Participants' specific suggestions included centralizing relevant information about services, implementing a coach or navigator to help advocate for access, and delivering care virtually. CONCLUSIONS: Participants highlighted barriers to access and made suggestions for improving supportive care that they believed would reduce the burden associated with trying to manage their cancer journey.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".