When van Mechelen's sequence of injury prevention model requires pragmatic and accelerated action: the case of para alpine skiing in Pyeong Chang 2018
Bibliographic record
Abstract
Results of the Paralympic Injury and Illness Surveillance Study noted an unusually high injury incidence rate (IR) in the sport of Para alpine skiing at the 2014 Sochi Paralympic Games (IR 48.3, a sixfold increase in acute injuries in comparison to the Vancouver 2010 Paralympic Games).1–3 There were likely several associated factors. Some were clearly modifiable such as the course design, number of training runs permitted on the course and the command and control structure between the technical and medical staff. Additionally, Paralympic officials recognised that careful monitoring of weather data and timely management of snow production, taking advantage of modern technology, had the potential to reduce injury risk. Thus, for the 2018 PyeongChang Paralympic Winter Games (the Games), the International Paralympic Committee (IPC) Medical Committee, in collaboration with the World Para Alpine Skiing (WPAS) sport technical staff, implemented a series of changes following Professor Willem van Mechelen’s ‘Sequence of (Injury) Prevention’ model4: The Paralympic Injury and Illness Surveillance Study carried out at the Sochi 2014 Paralympic Games demonstrated a dramatic …
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".