Understanding the Experiences of Physical Activity, Body Image, and Quality of Life in Young Adult Males Living with and beyond Cancer
Bibliographic record
Abstract
For young adults (YAs), a cancer diagnosis and subsequent treatments may result in physical changes that can negatively impact body image (BI) and health-related quality of life (HRQL). Physical activity (PA) is an evidence-based tool found to impact both BI and HRQL. However, most research has focused on the perspectives of older adults with breast or prostate cancer. No research has explored the experiences of PA, BI, and HRQL in YA males affected by cancer. A qualitative study was designed for YA males diagnosed with cancer between the ages of 20 and 39 years. Eligible participants were recruited through pre-existing exercise oncology studies, support organizations, and social media. Semi-structured interviews were conducted to understand participants’ experiences of PA, BI, and HRQL. All interviews were transcribed verbatim and analyzed using interpretive description. The participants were YA males (n = 7) with a mean age of 32.7 ± 4.0 years. Themes included a loss of identity due to cancer, building autonomy and identity using PA, and the “should” behind BI. PA for YA males living with and beyond cancer may support them in rebuilding their identity and BI. The development of exercise oncology resources for YA males may consider addressing BI through education or exercise prescription programs, with the goal of enhancing HRQL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".