The effect of a school based injury prevention program on physical performance in youth females
Bibliographic record
Abstract
Injury prevention (IP) programs can reduce injury in youth, however, little is known about their impact on athletic performance, especially in females. The purpose of this study was to examine the effects of a school curriculum IP program on movement skill and athletic performance in youth females. Ninety-two female students (age 14.0 ± 0.6 y, height 162.5 ± 5.8 cm, mass 57.1 ± 9.3 kg, intervention (INT) n = 43, control (CON) n = 49) participated in this study. The INT group completed a 23 week IP program whereas the CON group continued normal physical education class. Sprint, countermovement jump (CMJ), isometric mid-thigh pull (IMTP), y-balance, back squat assessment (BSA), and drop vertical jump (DVJ) were assessed. An independent-samples t-test revealed that the INT group performed significantly better on all tests at baseline (p = <0.05), except single-leg CMJ power. Ordered regression models showed the INT group were more likely to improve their score on the BSA and DVJ (OR = 0.14 and 0.20) compared to the CON group. Marginal analysis revealed a significantly greater increase in y-balance composite measures for the INT group [mean difference (95% CI) = 2.07 (0.48 to 3.66) and 2.66 (1.03 to 4.29), p < 0.05] and relative IMTP for the CON group [mean difference (95% CI) = −0.34 (−0.60 to −0.08), p < 0.05). These findings highlight that a long-term IP program integrated into the school curriculum can improve movement skill and balance in youth females.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".