doi:10.1155/2012/167651 Review Article “We’re All in the Same Boat”: A Review of the Benefits of Dragon Boat Racing forWomen Living with Breast Cancer
Bibliographic record
Abstract
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This narrative review summarizes findings from quantitative and qualitative research literature that has been published over the past 15 years since an initial, community-based pilot study first challenged the long-held medical belief that vigorous, upper-body exercise would lead to lymphedema in women who were at risk due to treatments for breast cancer. Dragon boat racing originated in China more than 2000 years ago and has become a popular recreational and competitive support around the world. From the advent of the world’s first breast cancer survivor dragon boat team, Abreast in a Boat launched in Vancouver, British Columbia, in 1996, there are now more than 140 breast cancer survivor dragon boat teams paddling and competing in 12 different countries. The wealth of quantitative and qualitative research that has ensued since that pilot study further supports the initial hypothesis that resistance exercise, for example, dragon boat paddling, is not only safe for women recovering from conventional breast cancer therapies but also shows that dragon boating has been embraced as a complementary exercise therapy by the cancer survivors participating in this magical sport. “In any adventure, who people are can be determined not by what happens to them but by how they deal with it.” MacPhee, 1994 1.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.024 |
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".