Barriers to Optimal End-of-Life Care for Adolescents and Young Adults With Cancer: Bereaved Caregiver Perspectives
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYAs; aged 15-39 years) with cancer frequently receive intensive measures at the end of life (EoL), but the perspectives of AYAs and their family members on barriers to optimal EoL care are not well understood. METHODS: We conducted qualitative interviews with 28 bereaved caregivers of AYAs with cancer who died in 2013 through 2016 after receiving treatment at 1 of 3 sites (University of Alabama at Birmingham, University of Iowa, or University of California San Diego). Interviews focused on ways that EoL care could have better met the needs of the AYAs. Content analysis was performed to identify relevant themes. RESULTS: Most participating caregivers were White and female, and nearly half had graduated from college. A total of 46% of AYAs were insured by Medicaid or other public insurance; 61% used hospice, 46% used palliative care, and 43% died at home. Caregivers noted 3 main barriers to optimal EoL care: (1) delayed or absent communication about prognosis, which in turn delayed care focused on comfort and quality of life; (2) inadequate emotional support of AYAs and caregivers, many of whom experienced distress and difficulty accepting the poor prognosis; and (3) a lack of home care models that would allow concurrent life-prolonging and palliative therapies, and consequently suboptimal supported goals of AYAs to live as long and as well as possible. Delayed or absent prognosis communication created lingering regret among some family caregivers, who lost the opportunity to support, comfort, and hold meaningful conversations with their loved ones. CONCLUSIONS: Bereaved family caregivers of AYAs with cancer noted a need for timely prognostic communication, emotional support to enhance acceptance of a poor prognosis, and care delivery models that would support both life-prolonging and palliative goals of care. Work to address these challenges offers the potential to improve the quality of EoL care for young people 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".