Exploring the Survivorship Experiences and Preferences for Survivorship Care Following Testicular Cancer: A Mixed Methods Study
Bibliographic record
Abstract
Abstract Background: Men with testicular cancer are an understudied subgroup of survivors due to a lack of engagement in supportive care research and usual care despite reporting unmet support care needs. The present study aimed to explore the survivorship experience of men with testicular cancer, as well as survivors’ attitudes toward, and preferences for, sport-based supportive care programming. Methods: Using a concurrent mixed methods approach, semistructured interviews with a convenient sample of testicular cancer survivors (n = 11) were conducted, along with a cross-sectional survey (n = 135) completed during routine oncology visits. Results: Inductive thematic analysis of qualitative data resulted in three core themes labeled “the developmental disruption," “the connectedness conflict," and “the way back to normal." Based on the descriptive analysis of survey data and qualitative feedback, testicular cancer survivors indicated preferences for sport-based supportive care programming that is offered outside of the hospital, in the evening, include a strength training component, and embedded psychoeducation throughout addressing common survivorship concerns related to physical health (eg, fertility and fatigue), managing emotions (eg, anxiety and low mood), and getting back on track. Conclusions: Findings from this study provide a foundation for the development of a gender-sensitized supportive care program tailored to the survivorship profile of men with testicular cancer.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 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".