Global Long-Term Care Research: A Scientometric Review
Bibliographic record
Abstract
Since the early 1960s, long-term care (LTC) has attracted a broad range of attention from public health practitioners and researchers worldwide and produced a large volume of literature. We conducted a comprehensive scientometric review based on 14,019 LTC articles retrieved from the Web of Science Core Collection database from 1963 to 2018, to explore the status and trends of global LTC research. Using CiteSpace software, we conducted collaboration analysis, document co-citation analysis, and keyword co-occurrence analysis. The results showed a rapid increase in annual LTC publications, while the annual citation counts exhibited an inverted U-shaped relationship with years. The most productive LTC research institutions and authors are located primarily in North American and European countries. A simultaneous analysis of both references and keywords revealed that common LTC hot topics include dementia care, quality of care, prevalence and risk factors, mortality, and randomized controlled trial. In addition, LTC research trends have shifted from the demand side to the supply side, and from basic studies to practical applications. The new research frontiers are frailty in elderly people and dementia care. This study provides an in-depth understanding of the current state, popular themes, trends, and future directions of LTC research worldwide.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.028 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.139 | 0.175 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".