Palliative Care Landscape in the COVID-19 Era: Bibliometric Analysis of Global Research
Bibliographic record
Abstract
Despite the increasing number of publications globally, the COVID-19 pandemic has underscored significant research gaps that should be resolved, including within PC-related research. This study aimed to map and understand the global trends in palliative care (PC)-related COVID-19 research and provide quantitative evidence to guide future studies. We systematically searched four databases between 1st January 2020 and 25th April 2022. The VOSviewer, Gephi, and R software were utilized for data analysis and results visualization. A total of 673 articles were identified from the databases between 1st January 2020 and 25th April 2022. Canada (6.2%), Australia (5.4%), and the United Kingdom (3.8%) were the most productive countries regarding articles published per million confirmed COVID-19 cases. A lack of international collaborations and an uneven research focus on PC across countries with different pandemic trajectories was observed. The PC research in question focused on cancer, telehealth, death and dying, and bereavement. This study's conclusions support the recommendation for international collaboration to facilitate knowledge and practice transformation to support countries with unmet PC needs during the pandemic. Further studies are required on the grief and bereavement support of families, healthcare professionals and patients with other life-threatening illnesses.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.087 | 0.456 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".