In the Eye of the Dragon: Women's Experience of Breast Cancer and the Occupation of Dragon Boat Racing
Bibliographic record
Abstract
BACKGROUND: Women with breast cancer are at risk of developing lymphedema following surgical and/or medical treatment of the disease. Recently, women have challenged traditional advice about limiting upper extremity activity to prevent lymphedema by participation in dragon boat racing. PURPOSE: In this qualitative pilot study, three women were interviewed about the meaningfulness of dragon boat racing in their lives. METHODS: The women were interviewed twice and their interviews analyzed using thematic analysis. RESULTS: Seven themes are discussed: attraction of dragon boat racing; physical and emotional well-being; competition; social support; transcendence/connectedness/oneness; re-occurrence of cancer; and, public awareness. Competition enabled the participants to rebuild self-confidence and to regain control over their physical health and emotional well-being. Balancing support and competition was key to finding satisfaction in this occupation. The women did not believe that dragon boat racing affected their risk for developing lymphedema. PRACTICE IMPLICATIONS: People who engage in the occupation of dragon boat racing find it meaningful and develop new coping strategies. Participating in dragon boat racing can decrease stress and shift the meaning of having breast cancer to a more positive view of the experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".