Self-Disclosure by Childhood Cancer Survivors and Its Psychological Effects
Bibliographic record
Abstract
The long-term survival rates for childhood cancers have continued to rise, making it critical to understand the long-term outcomes and the follow-up needs of those diagnosed with childhood cancer. Long-term survival is associated with both secondary illnesses and psychological distress; however, the self-disclosure of one’s history of childhood cancer could promote self-care and social support among childhood cancer survivors. Nevertheless, many childhood cancer survivors are reluctant to self-disclosure, particularly in Japan, as its cultural context emphasizes collective homogeneity rather than individualism. This qualitative, descriptive study aimed to understand the experiences of adult childhood cancer survivors regarding their self-disclosure of their disease history to their lovers, friends, and community. Between October 2017 and November 2018, 13 adults (9 men, 4 women; age range: 20–39 years) who had been diagnosed with childhood cancer and were five years or more past their last treatment participated in semi-structured interviews. A thematic analysis was conducted using the Steps for Coding and Theorization (SCAT). From the interview data, three concepts (i.e., Increased Desire for Self-Disclosure, Joy at Having Enriched the Lives of Themselves and Others, and Increased Expectations for Relationship Change and Disappointment), along with eight themes, were extracted. Self-disclosure was found to have both positive and negative effects, but the willingness to risk self-disclosure promotes the development of social and intimate relationships among childhood cancer survivors. Negative experiences or frustration from poor responses to self-disclosure can interfere with future self-disclosure attempts. Thus, self-disclosure could serve as a means of overcoming one’s disease. Healthcare providers involved in long-term follow-up should promote the benefits of self-disclosure in overcoming a childhood cancer diagnosis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".