‘Tackling’ rugby safety through a collective approach
Bibliographic record
Abstract
Commitment to seeking an evidence-informed approach When it comes to player welfare, Rugby Union governing bodies have committed to seeking and funding an evidence-informed approach. This involves using research to make informed decisions about policy, laws and injury prevention programmes. Over the last decade, a growing body of research has informed player safety, for example, modifications to scrum laws to reduce catastrophic head and neck injuries.1 However, major gaps remain, including tackle research focusing on the women’s game.2 A socioecological perspective It is well understood that player welfare, specifically injury prevention, is a complex issue. To effectively address these complexities and make a long-term impact requires a dynamic socioecological approach.3 ,4 An athlete operates within a socioecological structure (individual, interpersonal, organisational, community) that is influenced by a web of inter-related factors and actors, both of which change over time and/or when a factor/actor is modified (figure 1). Typically, injury prevention research will identify player level factors that influence injury risk (risk factors), and aim to modify these factors through behaviour change interventions. However, the socioecological view emphasises understanding contextual factors influencing implementation of such modifications. For example, if the behaviour intervention is a training programme, how much time is available to implement the training programme? Or, is the training programme appropriate for all sexes?
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".