Adult childhood cancer survivors’ narratives of managing their health: the unexpected and the unresolved
Bibliographic record
Abstract
PURPOSE: Currently, 80 % of children diagnosed with cancer will be cured. However, many of these survivors go on to develop long-term health problems or late effects related to their previous cancer and therapy and require varying degrees of lifelong follow-up care. The purpose of this study was to identify the different ways that adult survivors of childhood cancer manage their medical and psychological challenges. METHODS: Data from in-depth interviews with 30 adult survivors of a childhood cancer (9 to 38 years after diagnosis, currently 22 to 43 years of age, 60 % women) were analyzed using qualitative, thematic narrative analysis methods. RESULTS: The survivors had not expected the medical, psychological, and social challenges that arose over time and that often remained unresolved. Five narrative themes revealed distinct ways that survivors managed their health challenges: (1) trying to forget cancer, (2) trusting the system to manage my follow-up care, (3) being proactive about my health, (4) stumbling from one problem to the next, and (5) struggling to find my way. CONCLUSIONS: Variation exists in the ways in which childhood cancer survivors frame their health, their perceived significance of health challenges, strategies used to manage health, interactions with healthcare professionals and the health system, and parental involvement. IMPLICATIONS FOR CANCER SURVIVORS: This research provides novel insights that can be used to inform the development of patient-centered health services that promote the assessment and tailoring of care to the diverse ways survivors enact their agency, as well as their psychoeducational coping styles, therapeutic relationship needs, and information needs.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".