Impact Assessment of Sports Medicine Studies on Knowledge Production and Development
Bibliographic record
Abstract
In order to justify the investments made in research in the field of sports medicine, the outcomes and impacts of these investments should be assessed. The purpose of this study was to investigate the status and impact of sports medicine studies on the production and development of knowledge. In this descriptive study bibliometric and scientometric methods were used on 1145 scientific productions of sports medicine indexed in the Web of Science database. Data were analyzed through Excel Software, and cooperation maps were drawn using VOSviewer Software. According to the findings, the ratio of citations to the articles on the scientific productions of sports medicine was 23.17, which is higher than the clinical medicine area (6.8). The ratio of citations to the authors was 5.46% and 52 articles (4.54%) of the articles appeared without citations. The average impact factor of journals publishing papers was 3.9. Most of the articles were published with the collaboration of five authors. The results of the present study, based on a selected model and a combination of indicators of the UK’s and Canadian Capital Return frameworks (from the production and development dimension), generally highlighted the validity and effectiveness of all indicators, including activity, quality, and development. The results revealed the most significant impact of the number and quality of each of the indicators in sports medicine in this area.
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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 | 0.042 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".