An Online Evidence-Based Education Resource Is Useful and Can Change People’s Perceptions About Running and Knee Health
Bibliographic record
Abstract
OBJECTIVES: To (1) create and evaluate the usefulness of an online evidence-based education resource about running and knee health (eg, knee osteoarthritis) for the public and health care professionals, and (2) assess the online resource's effects on perceptions about running and knee health. DESIGN: Cross-sectional survey. METHODS: We created an online education resource (series of infographics) in 7 languages with community input. Then, we conducted a single-round online survey in which participants rated its usefulness and answered questions on perceptions about running and knee health before and after reading the infographics. RESULTS: Two thousand six hundred ninety-four participants (1291 members of the general public and 1403 health care professionals; 45.7% with knee osteoarthritis and 67.6% runners) from 60 countries viewed the infographics and responded to the postinfographics questions. The online resource was considered very useful, with a median rating of 9 out of 10. 23.2% of participants reported no change in their perceptions about running and knee health, 46.2% changed a little bit, 19.3% changed a moderate amount, and 11.3% changed a lot. Perceptions of running were more favorable after reading the infographics, especially about the effects of regular and frequent running on knee health, and running in individuals with knee osteoarthritis. Perceptions about running long distances were less favorable after the infographics. CONCLUSION: Our free online education resource was considered useful by both the public and health care professionals. Overall, the online resource led to more positive perceptions about recreational running and knee health. However, its effects on behavior change and running participation remain unknown.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".