What are the perceptions about running and knee joint health among the public and healthcare practitioners in Canada?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the perceptions of the general public and healthcare practitioners (HCP) in Canada about the relationship between running and knee joint health, and to explore HCP`s usual recommendations to runners with knee osteoarthritis (KOA). METHODS: Non-runners and runners (with and without KOA) and HCP completed an online survey regarding the safety of running for knee joint health. HCP also provided information related to usual clinical recommendations. Proportions of agreement were compared between non-runners and runners. RESULTS: A total of 114 non-runners, 388 runners and 329 HCP completed the survey. Overall, running was perceived as detrimental for the knee joint by 13.1% of the general public, while 25.9% were uncertain. More uncertainty was reported regarding frequent (33.9%) and long-distance (43.6%) running. Statistical analyses revealed greater proportions of non-runners perceiving running negatively compared with runners. Overall, 48.4% believed that running in the presence of KOA would lead to disease progression, while 53.1% believed running would lead to premature arthroplasty. In HCP, 8.2%, 9.1% and 22.2% perceived that running in general, running frequently, or running long-distances are risk factors for KOA, respectively. 37.1% and 2.7% of HCP typically recommended patients with KOA to modify their running training or to quit running, respectively. CONCLUSION: High rates of uncertainty among the general public and HCP in Canada outline the need for further studies about running and knee joint health. Filling knowledge gaps will help inform knowledge translation strategies to better orientate the general public and HCP about the safety of running for knee joint health.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".