Injury Prevention in Female Athletes: Defining the Boundaries of Scientific Literature
Bibliographic record
Abstract
Purpose: This study aims to perform a bibliometric analysis centered on recent advancements in injury prevention for female athletes. Methods: The study employed Excel, VOSviewer, and the bibliometric R-package tools to analyze and evaluate relevant records obtained from the Web of Science (WOS) database, using a reliable search methodology. Results: From the WOS database, a collection of 10 560 scientific records was acquired using specific keywords, covering the period between 2010 and 2023. These records were subjected to content analysis, revealing the prevalent themes in this research area. Noteworthy topics included risk, risk factors, prevention, women, exercise, physical activity, epidemiology, injuries, performance, injury, strength, and health. The investigation also indicated that the journals “American journal of sports medicine” and “journal of athletic training” demonstrated the highest level of activity in this field. Regarding research productivity, developed countries, such as the United States and Canada stood out as the most prolific contributors. Moreover, the study recognized Gregory D Myer as the most active author in this area. Conclusion: The convergence of injury prevention in female athletes continues to be a subject of significant research attention. This study highlights that the bulk of the literature on this subject originates from researchers in developed countries. However, it is crucial to recognize that a substantial segment of the global population, particularly in developing nations, lacks sufficient representation in research related to early life psychology concerning exercise and physical activity.
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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 | 0.044 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.098 | 0.083 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".