Methodological characteristics and future directions for plyometric jump training research: A scoping review update
Bibliographic record
Abstract
The aim of this scoping review was a) to update a previous review on the main methodological characteristics and shortcomings in the plyometric jump training (PJT) literature, and b) to recommend, in light of the identified methodological gaps, future research perspectives. We searched four electronic databases. From 6128 potentially relevant articles, 420 were considered eligible for inclusion. As an update of a previous review, this represents an increase of ~200 articles, illustrating that this field of research is growing fast. However, the relative "quality" or shortcomings were similar when compared to the preceding scoping review. In the current article, the main identified shortcomings were an insufficient number of studies conducted with females, individual sports, and high-level athletes (~22%, ~7%, and ~14% of overall studies, respectively); insufficient description of training prescription (~54% of studies); and studies missing an active/passive control group and a randomized group allocation process (~37% and ~24% of overall studies, respectively). Furthermore, PJT was often combined with other training methods and added to the participants' regular training routines (~50% and ~35% of overall studies, respectively). The main outcomes of this scoping review urge researchers to conduct PJT studies of high methodological quality (eg, randomized controlled trials) to get trustworthy evidence-based knowledge. In addition, owing to the limited research conducted with females, individual sports, and high-level athletes, more studies are needed to substantiate the available findings. Finally, the identification of cohort-specific PJT dose-response relations which elicit optimal training effects still needs to be identified, particularly in the long term.
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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 | 0.267 | 0.489 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.053 | 0.046 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".