A Warm-Up Program to Reduce Injuries in Youth Field Hockey Players: A Quasi-Experiment
Bibliographic record
Abstract
CONTEXT: Field hockey is popular worldwide; however, it entails a risk of injury. Injuries hamper players' participation in the sport and impose a burden on public health. OBJECTIVE: To investigate the effectiveness of a structured exercise program among youth field hockey players on the injury rate, severity, and burden. DESIGN: Quasi-experimental study. SETTING: On field during 1 season of field hockey (October 2016 through June 2017). PATIENTS OR OTHER PARTICIPANTS: A convenience sample of 22 teams (291 players): 10 teams (135 players, mean age = 11.5 years [95% confidence interval (CI) = 11.2, 11.7 years]) in the intervention group and 12 teams (156 players, mean age = 12.9 years [95% CI = 12.6, 13.2 years]) in the control group. INTERVENTION(S): The Warming-up Hockey program, a sex- and age-specific, structured, evidence-informed warm-up program consisting of a preparation phase (ie, agility and cardiovascular warm-up exercises), movement skills (ie, stability and flexibility exercises), and sport-specific skills (ie, speed and strength exercises in field hockey situations). Participants in the control group performed their usual warm-up routines. MAIN OUTCOME MEASURE(S): Injury rate (ie, the number of injuries per 1000 player-hours of field hockey exposure), severity (ie, days of player time-loss), and burden on athletes' availability to play (ie, days of time loss due to injury per 1000 player-hours of field hockey exposure). RESULTS: .73). The burden of injuries on players' field hockey participation was lower in the intervention group (difference of 8.42 [95% CI = 4.37, 12.47] days lost per 1000 player-hours of field hockey). CONCLUSIONS: Exposure to the Warming-up Hockey program was not significantly associated with a lower injury rate. No reduction was observed in the severity of injuries alone; however, the burden of injuries on players' field hockey participation was lower in the intervention group.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".