Nationwide sports injury prevention strategies: a scoping review
Bibliographic record
Abstract
National strategies to prevent sports injuries can potentially improve health outcomes at a population level and reduce medical costs. To date, a compilation of the strategies that countries have attempted, and their effectiveness, does not exist. This scoping review sets out to: identify nationwide attempts at implementing sports injury prevention strategies; examine the impact of these strategies; and map them onto the Translating Research into Injury Prevention Practice (TRIPP) framework. Using Levac's scoping review method, we: (a) identified the research questions, (b) identified relevant studies, (c) identified the study selection criteria, (d) charted the data, and (e) reported the results. A search of MEDLINE, Scopus, SPORTDiscus, CINAHL, and Web of Science databases for articles published pre-June 2019 was conducted. We identified 1794 studies and included 33 studies (of 24 strategies). The USA (n = 7), New Zealand (n = 4), Canada (n = 3), the Netherlands (n = 3), Switzerland (n = 2), Belgium (n = 1), France (n = 1), Ireland (n = 1), South Africa (n = 1), and Sweden (n = 1) have implemented nationwide sports injury prevention strategies with 29 (88%) of the included studies demonstrating positive results. Mapping the strategies onto the TRIPP framework highlighted that only four (17%) of the 24 included strategies reported on the implementation context (TRIPP Stage 5), suggesting an important reporting gap. Nationwide sports injury prevention efforts are complex, requiring a multidimensional approach. Future research should report intervention implementation data; examine the implementation context early in the research process to increase the likelihood of real-world implementation success; and could benefit from incorporating qualitative or mixed research methods.
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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 | 0.034 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".