Sources of Stress for Breast Cancer Survivors Involved in Dragon Boating: Examining Associations with Treatment Characteristics and Self-Esteem
Bibliographic record
Abstract
OBJECTIVES: This study sought to (1) identify common stressors faced by breast cancer survivors involved in dragon boating, (2) examine the conceptual and statistical factor groupings of the stressors, (3) identify differences in stressor factors based on treatment characteristics, and (4) examine the associations between stressor factors and two indicators of self-esteem. METHODS: Survivors (n = 470) involved in dragon boating completed a survey assessing stressor frequency, stressor intensity, stressor valence, physical self-worth, and global self-esteem, along with demographic and cancer treatment information. RESULTS: An exploratory factor analyses (EFA) using maximum likelihood extraction with oblique rotation revealed a four-factor solution that included physical, emotional, social, and exercise-related stressors. Exercise-related stressors were reported more frequently and intensely but were appraised positively by most survivors. The physical, emotional, and social stressors were perceived predominantly as negative. Findings also revealed that physical and emotional stressors and exercise-related stressors were correlates of physical self-worth (R(2) = 0.26). Emotional, social, and exercise-related stressors were significant correlates of global self-esteem (R(2) = 0.11). Cancer treatments were also associated with the experience of stressors, with the strongest effects reported for chemotherapy treatment. CONCLUSIONS: Overall, the results demonstrate that participants experienced many stressors but that exercise-related stressors were viewed as more adaptive and were positive correlates of self-esteem processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".