LIVING WELL AFTER CANCER: THE IMPACT OF SOCIAL SUPPORT AND PRODUCTIVE LEISURE
Bibliographic record
Abstract
"It is now recognized that the ""cancer experience"" extends beyond diagnosis, treatment, and end-of-life care. Relative to individuals who have not faced a cancer diagnosis, cancer survivors report increased mental health concerns and lowered physical and psychological well-being (Langeveld et al., 2004). Health-related quality of life encompasses overall physical (e.g., energy, fatigue, pain, etc.) and psychological functioning (e.g., emotional well-being, etc.), as well as general health perceptions (Hays & Morales, 2001). Nayak and colleagues (2017) reported that 82.3% of cancer patients had below-average quality of life scores, with the lowest scores found in the general, physical, and psychological well-being domains. Research suggests that various positive lifestyle variables, including social connectedness, leisure activity, and mindfulness practices are associated with increased quality of life in cancer patients (Courtens et al., 1996; Fangel et al., 2013; Garland et al., 2017). In this study, 350 cancer survivors completed an online questionnaire package that included a detailed demographic questionnaire with medical and online support and leisure activity questions. Additional measures were included to assess quality of life (QLQ-C30; Aaronson et al., 1993), social connectedness (Social and Emotional Loneliness Scale for Adults, SELSA-S; DiTommaso et al., 2004), and mindfulness (Adolescent and Adult Mindfulness Scale, AAMS; Droutman et al., 2018). Results show that increased QOL is predicted by increased medical support, lower family loneliness, self-acceptance, and engaging in a variety of leisure activities. Encouraging family support, including the patient in the decision-making process, encouraging a variety of physically possible leisure activities, and normalizing negative emotions surrounding diagnosis and disease symptoms are all ways that overall QoL can be improved."
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".