Childhood cancer survivorship: barriers and preferences
Bibliographic record
Abstract
Objective: Many survivors are disengaged from follow-up, mandating alternative models of survivorship-focused care for late effects surveillance. We explored survivors' barriers to accessing, and preferences for survivorship care. Methods: We invited Australian and New Zealand survivors of childhood cancer from three age groups: <16 years (represented by parents), 16–25 years (adolescent and young adults (AYAs)) and >25 years ('older survivors'). Participants completed questionnaires and optional interviews. Results: 633 survivors/parents completed questionnaires: 187 parents of young survivors (mean age: 12.4 years), 251 AYAs (mean age: 20.6 years) and 195 older survivors (mean age: 32.5 years). Quantitative data were complemented by 151 in-depth interviews. Most participants, across all age groups, preferred specialised follow-up (ie, involving oncologists, nurses or a multidisciplinary team; 86%–97%). Many (36%–58%) were unwilling to receive community-based follow-up. More parents (75%) than AYAs (58%) and older survivors (30%) were engaged in specialised follow-up. While follow-up engagement was significantly lower in older survivors, survivors' prevalence of late effects increased. Of those attending a follow-up clinic, 34%–56% were satisfied with their care, compared with 14%–15% of those not receiving cancer-focused care (p<0.001). Commonly reported barriers included lack of awareness about follow-up availability (67%), followed by logistical (65%), care-related beliefs (59%) and financial reasons (57%). Older survivors (p<0.001), living outside major cities (p=0.008), and who were further from diagnosis (p=0.014) reported a higher number of barriers. Conclusions: Understanding patient-reported barriers, and tailoring care to survivors' follow-up preferences, may improve engagement with care and ensure that the survivorship needs of this population are met.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".