How Advocates Can Support Young Adults Living With Cancer and Their Transition to Palliative Care
Bibliographic record
Abstract
While the cancer advocacy community has been pivotal in progressing oncology care, supporting young adults with advanced cancer transitioning to palliative care continues to be a complex challenge. Palliative care services may not be offered by healthcare providers or engaged by young people themselves. This is in the face of the recognized value that palliative care can provide young people and their families. The purpose of this study was to explore what cancer advocates can do to support young adults (18-39 years of age) with advanced cancer in their transition to palliative care. A community-based research perspective supported engagement with members of the #AYACSM (Adolescent and Young Adult Cancer Societal Movement) from the United States and Canada through social media. Analysis was guided by a reflexive thematic analysis approach to articulate four action-oriented themes: advocate for advances in the delivery of care; support healthcare provider education; mobilize knowledge and share stories; and leverage technology for advocacy efforts. Young adult cancer advocacy must span the continuum of cancer care from prevention to end-of-life. There exist gaps in advocacy efforts surrounding support for young people in their transition to and the integration of palliative care services. Creative and innovative advocacy approaches are needed. This study also showed opportunities for conducting qualitative research through an existing online community as an approach conducive to community-based 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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".