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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".