Quality of Life of Adolescents and Young Adult Survivors of Childhood Cancer: A Qualitative Study
Bibliographic record
Abstract
AIMS: To analyse how adolescents and young adults who survived childhood cancer experience their quality of life and to explore their meanings and conceptualisations of quality of life. DESIGN: Narrative design following Squire's method. METHODS: In-person and virtual recruitment, due to the global COVID-19 pandemic, was carried out in Brazil between May and November 2021. Eighteen semi-structured virtual interviews were conducted with childhood cancer survivors. These data were analysed using reflexive thematic analysis and employing a triangulation of investigators. RESULTS: Four subthemes were identified, encompassing participants' perceptions and meanings of quality of life. Through the reflection and synthesis of these subthemes, a central theme emerged entitled 'New self', capturing the profound impact of the cancer and survival experience on every aspect of the self, along with participants' sense of having gained 'a new life' and a 'second chance'. CONCLUSION: The study provides a rich and nuanced understanding of quality of life for childhood cancer survivors. The results highlight that after all the changes in life due to cancer, integrating the new self becomes a central aspect of quality of life for survivors. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Recognising the profound shifts in self-perception that survivors of childhood cancer undergo, nurses should engage early in empowering patients and families, offering education, space and support to help adolescents and young adults grow throughout their journey. IMPACT: The richness and depth inherent in qualitative data on quality of life can inform the development of care standards and health policies for survivors, guide the allocation of strategic resources and shape the development of plans and interventions focusing on childhood cancer survivors. REPORTING METHOD: The COREQ checklist was used. PATIENT OR PUBLIC: No patient or public contribution.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".