Unveiling perspectives on the psychosocial impacts of childhood cancer survival on young adult survivors’ reassimilation journey: A qualitative exploration
Bibliographic record
Abstract
Background: Approximately 82% of children with childhood cancer survive more than five years after diagnosis. Living as a cancer survivor elicits a new reality that can include psychosocial impacts. These psychosocial impacts interact collectively, especially regarding reassimilation, and are rarely explored. Objective: To explore the psychosocial impacts of surviving childhood cancer and reassimilation back into society in young adult survivors of childhood cancer. Methodology: Individual in-depth semi-structured interviews were conducted with childhood cancer survivors and explored psychosocial aspects associated with returning to work, school, and social environments after remission. Interpretive phenomenological analysis was conducted once interviews were manually transcribed. A group interview with survivors was held to discuss the study's findings and interpretation. Results: Individual interviews and the group interview revealed three major themes: outlook on reassimilating, outlook on coping, and outlook on cancer. Conclusions: This work is a first step to understanding how survivors' personal outlook on coping and healthcare system barriers play influential roles in reassimilation following cancer treatment. Survivors expressed the need for reliable survivorship information and improved communication with healthcare providers regarding what to expect, so they could feel prepared for life post-cancer. These aspects need to be explored more deeply through other qualitative studies.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".