Análisis de la investigación en actividades físicas y su impacto en la salud pública: una evaluación integral
Bibliographic record
Abstract
Research on the interaction between physical activity and public health is of growing interest due to its significant influence on the prevention of chronic diseases and the promotion of comprehensive well-being. This bibliometric analysis aims to provide a comprehensive view of the evolution of research in this field, identifying key thematic areas, geographical distribution of scientific production, and its impact on public health policies and clinical practices. The results reveal an increase in the volume of research, reflecting a deeper recognition of the benefits of physical activity on physical and mental health. Growth has been noted in the variety of research approaches, including studies on specific population groups and diverse contexts. These findings suggest an interest in how physical activity can improve quality of life and health at the individual and community levels. The study highlights the contribution of physical activity in the formulation of public health policies and the promotion of active lifestyles. The methodology is based on a systematic review of scientific literature from 2002 to 2023, using databases such as Scopus, with inclusion criteria focused on studies that examine the correlation between variables. Research shows that the United States leads in scientific production, followed by the United Kingdom, Australia, Brazil, Canada and China, with growing participation from countries such as Norway and Mexico.
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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 | 0.062 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.097 | 0.118 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".