SCOPUS-BASED BIBLIOMETRIC ANALYSIS OF PUBLICATION ACTIVITY IN THE FIELD OF HEALTHY AGING IN 2013-2022
Bibliographic record
Abstract
Introduction: Life expectancy is getting longer, and the proportion of the elderly population is increasing. Therefore, the concept of healthy aging gains importance and attracts attention in the scientific community. This article presented a ten-year bibliometric analysis of articles on healthy aging in the Scopus database. Methods: The Scopus database was used for the bibliometric analysis. The publication list was created using the keywords «aging well» and «healthy aging.» The number of articles, active countries-journals, frequent keywords, prolific authors, and funding sources were defined. Results: An upward trend was observed in the number of articles related to healthy aging between 2013 and 2022. The five leading countries in publication activity were the United States, China, the United Kingdom, Germany, and Canada, respectively. The most prolific authors were Ferrucci, L., Franceschi, C., Evans, M.K., Bennett, D.A., and Deary, I.J. The five most active journals were Plos One, Scientific Reports, International Journal of Molecular Sciences, International Journal of Environmental Research and Public Health, and Frontiers in Aging Neuroscience. Conclusion: Bibliometric analysis is a valuable method for assessing global trends in producing scientific literature on particular topics. This study revealed an upward trend in articles on healthy aging over time, indicating an increasing interest and focus on this topic. As the elderly population grows, it is anticipated that interest in healthy aging will progressively increase. It will be advantageous for researchers interested in this field to establish collaborations with prominent authors and institutions. Thus, they will canalize their future investigation in the proper direction.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.192 | 0.244 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| 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".