The effects of single versus multiple training sessions on the motor learning of two Krav Maga strike techniques, in women
Bibliographic record
Abstract
BACKGROUND: Experts of the Krav Maga (KM) self-defense system propose that KM techniques are based on simple body movements which are suggested to be learned rapidly and retained. This study investigated the acquisition, retention, and further improvement with additional training of two KM strike techniques among novice female practitioners: straight punch and defensive kick. METHODS: Sixteen healthy females (age: 23 ± 3.7 years) without any previous martial arts/self-defense experience volunteered to participate. All participants received an initial 30-min instruction session (AQ), taught by a certified KM instructor, where each technique was deconstructed into three checkpoints (defined as a component of the entire movement) for learning. Participants were divided into two groups, one of which received additional training. Several kinematic and kinetic measures were recorded at four timepoints: immediately before AQ, immediately after AQ, 5 days after AQ, and 12 days after AQ. RESULTS: Results suggest that both techniques were learned rapidly, as checkpoint performance was significantly improved after AQ. Kick velocity and impact force also increased significantly after AQ; however, these measures did not change after AQ for the punch technique. Additional training did not improve either punch or kick performance beyond that learned during AQ. CONCLUSION: The findings from this study suggest that a single training session may be sufficient to learn and retain KM strike techniques relatively permanently; and the acquisition of the kick technique may lead to concomitant improvements in kick velocity and impact force.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".