Dragon boat racing and health-related quality of life of breast cancer survivors: a mixed methods evaluation
Bibliographic record
Abstract
BACKGROUND: Breast cancer survivors who participate in physical activity (PA) are reported to experience improved health-related quality of life (HRQOL). However, the quantitative research exploring the relationship between the team-based activity of dragon boat racing and the HRQOL of breast cancer survivors is limited. Given the rising number of breast cancer survivors, and their growing attraction to dragon boating, further exploration of the influence of this activity on HRQOL is warranted. METHODS: This study is designed to: 1) quantitatively assess whether and how breast cancer survivors' participation in a season of dragon boat racing is related to HRQOL and 2) qualitatively explore the survivors' lived experience of dragon boating and how and why this experience is perceived to influence HRQOL. A mixed methods sequential explanatory design was used with the purpose of complementing quantitative findings with qualitative data. Quantitative data measuring HRQOL were collected at baseline and post-season (N=100); semi-structured qualitative interviews were used to elicit a personal account of the dragon boat experience (N=15). RESULTS: Statistically significant improvements were shown for HRQOL, physical, functional, emotional and spiritual well-being, breast cancer-specific concerns and cancer-related fatigue. A trend towards significance was shown for social/family well-being. Qualitative data elaborated on the quantitative findings, greatly enhancing the understanding of how and why dragon boat racing influences HRQOL. CONCLUSIONS: The use of a mixed methods design effectively captured the complex yet positive influence of dragon boating on survivor HRQOL. These findings contribute to a growing body of literature supporting the value of dragon boat racing as a viable PA intervention for enhancing survivor HRQOL.
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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 | 0.041 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".