Os efeitos do treino de resistência no excesso de peso: uma análise bibliométrica
Bibliographic record
Abstract
Objective: The aim of this study is to provide a thorough bibliometric analysis of resistance training methods in populations that are overweight. The objective is to present a comprehensive analysis of the present status of research on this particular topic and provide valuable recommendations to practitioners who are involved in this area. Materials and methods: We conducted a comprehensive analysis of the scientific literature on resistance training in overweight populations, covering the period from 1995 to 2024. A comprehensive search was performed in the Web of Science database using the keywords "resistance training" and "overweight." The search encompassed all pertinent information contained within the articles. The data were produced in BibTex format and then entered into the Bibliometrix program for analysis. Results: The examination showed that 60 countries and 281 periodicals published the 666 papers analyzed in this study between 1995 and 2024. Notable magazines include "Medicine and Science in Sports and Exercise" and the "Journal of Strength and Conditioning Research." The results also indicate substantial author collaboration and strong cooperation across countries, with the United States, Canada, Brazil, Australia, and Iran being the primary collaborating nations. Conclusions: Given the persistently high incidence of overweight, it is imperative to investigate exercise treatments that are effective. Recent research has indicated a growing interest in using resistance training as a means of addressing overweight issues. This upward trend is projected to persist in the future. This emphasizes the gravity of obesity as a matter of public health and showcases the efficacy of resistance training as an intervention strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.154 | 0.197 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| 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".