Continued Attendance in Long-Term Follow-Up Care Among Childhood Cancer Survivors
Bibliographic record
Abstract
While survival rates for childhood cancer have significantly improved, many survivors continue to experience late effects, making their health management a growing priority in survivorship care. To address these complications, systems for long-term follow-up care have been established; however, low attendance rates remain a concern, impeding the early detection and timely treatment of late effects. The objective of this qualitative descriptive study was to identify factors related to continued attendance in long-term follow-up care among childhood cancer survivors. Semi-structured interviews were conducted with 14 individuals aged between 20 and 39 years who had been diagnosed with childhood cancer and with a post-treatment period that has lasted more than five years. The data were analyzed using a content analysis method. Findings revealed that continued attendance was motivated by an understanding of the importance of early detection and a desire to reduce anxiety about potential late effects. In addition, the presence of a primary physician who served as an emotional anchor, as well as the desire to report on their growth and ability to lead a healthy social life, were also motivating factors for continued attendance. Furthermore, survivors had a heightened awareness of self-management and were motivated by a sense of appreciation toward their parents, who had supported their journey toward independence through continued follow-up care. Conversely, for some survivors, follow-up visits had become habitual and integrated into daily life, regardless of conscious intent.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".