Can Design Help Mitigate Running-Related Injuries?
Bibliographic record
Abstract
Long-distance recreational running is a popular form of exercise \nenjoyed by many people across Canada. With a plethora of well-understood \nphysical and mental health benefits, it is no surprise that running is so popular. \nThese benefits, coupled with a low barrier to participation, makes running an \nattractive form of exercise for many. \nWhile running may be a healthy way to stay active, many runners will inevitably \nsustain a running-related injury. While studies show varying degrees of injury \nprevalence, many indications point about 65%. These injuries often prevent \npeople from running, which can have an impact on an individual's physical and \nmental health. Running is known to help prevent lifestyle-related diseases such \nas cardiovascular disease, diabetes, and certain forms of cancer. Thus, keeping \nindividuals running carries health benefits to the athlete but it may also carry \nimmense socioeconomic benefits by reducing the burden on the Canadian \nhealthcare system. \nThe current research aims to review the current state of knowledge as it \npertains to the physical and mental health benefits associated with running, \nrunning-related technologies, and running-related injuries. Primary research \nwas conducted in order to understand perceptions of and attitudes toward \nrunning injuries. The insights derived from the secondary and primary research \ninitiatives were synthesized to yield 3 injury-prevention principles designed to \nmitigate running related injuries through the use of technology.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".