Testicular cancer survivors' behaviours, preferences, attitudes, and enablers and barriers to engaging in physical activity and supportive care
Bibliographic record
Abstract
Testicular cancer (TC) is the most prevalent male cancer diagnosed in young men (aged 15-39). High survival rates have resulted in a growing population of survivors in need of supportive care, however, engagement from men has been low. This may be due to a lack of age appropriate, gender-sensitive, and targeted initiatives. One way to engage men is through sport, which has been successfully leveraged in non-cancer populations as a gender-sensitive platform for providing targeted initiatives. Yet little is known about TC survivors' behaviours, preferences, and enablers and barriers to engaging in physical activity and supportive care. This study aimed to address this knowledge gap by surveying TC survivors during routine oncology visits. TC survivors (N=60, Mage = 32.45, SD = 8.55 years) reported being active (71% meeting recommended guidelines), had a history of sport participation (98%), and were interested in engaging in a sport-based supportive care program (69%). Mental health was perceived as 'good' or 'very good' by 75.5% of survivors, however, 76% reported distress in three or more survivorship areas (e.g. fear of recurrence). Lack of access to facilities, information, programming, and support were reported as main barriers to engagement. Descriptive statistics and Pearsons r correlations will be presented to characterize the sample and describe relationships between individual characteristics, physical activity preferences, and reported enablers and barriers. Study findings will inform the development of a TC specific supportive care program that is age appropriate, gender sensitive, and evidence based, aimed at addressing the survivorship needs of TC survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".