Physical Education Research Trends during Pre-Pandemic and Pandemic Period
Bibliographic record
Abstract
This study deals with a comprehensive bibliometric analysis of Physical Education research trends published from 2001 to 2023, based on 1,184 publications indexed in the Scopus® database. Using Excel and VOSviewer, key metrics such as publication trends, geographic distribution, citation impact, and collaboration networks were analyzed. Results indicate a strong concentration of research output in Western countries particularly the United States, United Kingdom, Canada, and Australia where studies were frequently published in high-impact journals and exhibited notable citation performance. Thematic network analysis revealed both the persistence of core research areas and the emergence of new themes during the COVID-19 pandemic, reflecting the field’s adaptability and resilience. Dominant topics included physical activity, Physical Education teaching, risk factors among university students, youth development, academic achievement, and teacher attitudes. These findings underscore the urgent need for inclusive, evidence-based strategies to enhance student well-being through Physical Education. The study concludes by advocating for stronger international collaboration and the sustained emphasis on foundational themes, particularly physical activity, to inform globally relevant curricula and programs that remain responsive to dynamic educational and health landscapes.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.091 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".