Long-Term Care Research in the Context of COVID-19 Pandemic: A Bibliometric Analysis
Bibliographic record
Abstract
Despite the increasing awareness of long-term care (LTC) research after the outbreak of COVID-19 pandemic, little attention was given to quantitatively describe the evolution of the research field during this period. A total of 1024 articles retrieved from the Web of Science Core Collection database were systematically analyzed using CiteSpace visualization software. The overall characteristics analysis showed that, in the context of the pandemic, attention to LTC research increased significantly-over 800 articles were published in the past two years. The USA, Canada, Italy, and England formed the leading LTC research group, which was consistent with the conclusions of existing bibliometric studies on LTC research before the outbreak. A rigorous analysis based on a dual perspective of references and keywords was applied to reveal that, compared with previous studies, in the context of the pandemic, the focus shifted from the mental and physical health status of older adults in need of LTC to the impact of the pandemic on those of older adults in LTC facilities, from the prevention of general epidemics to the prevention and response of significant public health emergencies, from providing and paying for LTC to strategies for LTC facilities to improve the quality of LTC and well-being of their residents during the pandemic. These findings can provide help and reference for academics, civil folks, and LTC practitioners, as well as help with the sustainable development of LTC research in the context of COVID-19 pandemic.
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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.250 | 0.314 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".