748 FO31 – Evaluating an injury prevention program (Prep-to-Play) in 2713 women and girls playing community Australian football: a hybrid implementation-effectiveness, stepped-wedge cluster randomised controlled trial
Bibliographic record
Abstract
Background Prep-to-Play, an injury prevention program, was co-designed with the Australian Football League for women’s Australian Football. Online resources were distributed to coaches in 2019, but coaches reported low confidence to use Prep-to-Play. A supported implementation strategy was devised, to enhance Prep-to-Play use. Objective To compare Prep-to-Play use and injury rates between unsupported and supported implementation. Design Hybrid implementation-effectiveness (type-III) stepped-wedge, cluster randomised controlled trial. Setting Community women’s Australian Football. Participants 165 teams [under-16 to senior, 2713 players]. Interventions Ten geographically-separated clusters (≥14 teams) began the unsupported phase and transitioned to the supported phase in 2021/2022 (random allocation to one-of-five dates). In the supported phase, 56 trained physiotherapists provided a workshop and two support visits for coaches/team leaders (Fig1). Outcomes Team representatives reported weekly Prep-to-Play use, knee injuries and head impacts. Prep-to-Play ‘use’ was defined as completing ≥75% of program elements (≥6/8 warm-up, ≥2/3 strength, ≥1 skills, Fig1), in ≥two-thirds of sessions. Anterior cruciate ligament (ACL) injuries were medically confirmed, and concussions were confirmed medically (60%) or via physiotherapist phone assessment. Prep-to-Play use and injury rates were compared between supported and unsupported phases (adjusted for clustering, time, age-group, region, competition-level). Results 134 teams received the workshop and two support visits. Weekly Prep-to-Play use increased from 15% (95%CI: 11% to 19%) to 35% (30% to 41%) following supported implementation (Odds Ratio 3.1, 2.1 to 4.7). Accounting for background time trends not associated with the intervention, injury (ACL, knee, concussion) incidence reduced per additional week spent in the supported phase, with a relative-risk reductions of ~3% per week (Table1). Estimated reductions were not statistically significant. Based on findings from descriptive analysis, we considered first-order fractional polynomials to model background trends in outcomes over time that were not associated with intervention exposure. Fractional polynomials provide a simple but flexible way to model trends in continuous variables and include a log transformation and a linear trend as special cases. For all injury types, we used the Akaike Information Criterion (AIC) to choose the best fractional polynomial fit for modelling the background time trend. Injury models were adjusted for team (random effect), time (study week as continuous variable), age-group (reference: senior, versus junior), region (reference: metropolitan, vs regional) competition-level (reference: top division, versus other divisions). Cluster-week and team-week random effects as per the primary outcome were trialed to capture within-period correlation. However, models failed to converge due to the low number of injuries reported. Conclusion A physiotherapist-led supported implementation resulted in a three-fold increase in odds of Prep-to-Play use. The sport-specific injury prevention program including warm-up, strength, and contact activities may reduce ACL and concussion injuries in community women’s Australian Football.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".