Research Status and Trends in Retirement Communities: A bibliometric Analysis
Bibliographic record
Abstract
In the context of global aging, the study of retirement communities has attracted the attention of scholars. However, there needs to be more systematic bibliometric studies. This study aims to sort out the current research status of the retirement community and explore research trends. This study adopts a bibliometric approach, using the Web of Science Core Collection (WoSCC) as a database, VOSviewer to analyze research collaboration networks, and CiteSpace to analyze keyword co-occurrences. The study’s conclusion showed that the significant scholars are Ayalon, Resnick, Bennett, Royall, Courtin, and Knapp. The major journals are the Journal of the American Geriatrics Society, Gerontologist, and Journal of Applied Gerontology.The main research areas are Geriatrics Gerontology, Public Environmental Occupational Health, and Nursing. The primary research countries are the United States, Canada, and China, with the United States leading the way. The study found that the author’s research collaboration network was more active, with Bolling as the bridge author. Institutional collaboration networks were also active, with the University of Washington’s research collaboration network being the most prominent. The research collaboration networks between countries (regions) needed to be more expensive. There are three possible research trends in retirement community research: (1). naturally occurring retirement community (NORC); (2). aging in place; (3). continuing care retirement community (CCRC); (4). physical activity.
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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.172 | 0.222 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".