Geriatric and Gerontology Research: A Scientometric Investigation of Open Access Journal Articles Indexed in the Scopus Database
Bibliographic record
Abstract
BACKGROUND: Scientometric analyses of specific topics in geriatrics and gerontology have grown robustly in scientific literature. However, analyses using holistic and interdisciplinary approaches are scarce in this field of research. This article aimed to demonstrate research trends and provide an overview of bibliometric information on publications related to geriatrics and gerontology. METHODS: We identified relevant articles on geriatrics and gerontology using the search terms "geriatrics," "gerontology," "older people," and "elderly." VOSviewer was used to perform bibliometric analysis. RESULTS: A total of 858 analyzed articles were published in 340 journals. Among the 10 most contributory journals, five were in the United States, with the top journal being the Journal of the American Geriatrics Society. The United States was the leading country in research, followed by Japan, Canada, and the United Kingdom. A total of 5,278 keywords were analyzed. In the analysis of research hotspots, the main global research topics in geriatrics and gerontology were older adults (n=663), education and training (n=471), and adults aged 80 years (n=461). These were gradually expanded to include areas related to caring for older adults, such as geriatric assessments (n=395). CONCLUSION: These results provide direction for fellow researchers to conduct studies in geriatrics and gerontology. In addition, they provide government departments with guidance for formulating and implementing policies that affect older adults, not only in setting academic and professional priorities but also in understanding key topics related to them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".