Academic and Online Attention to Palliative Care: A Bibliometric and Altmetric Perspective
Bibliographic record
Abstract
Objective: With a combined bibliometric and altmetric study, we aimed to provide a visually detailed perspective on palliative care, which is attracting increasing attention from academia and society. We also evaluated the relationship between supporting and contrasting citation counts and the altmetric attention score (AAS) for the first time in the literature. Methods: Web of Science (WoS) database and Altmetric.com website were used to create Top100 (T100) citation and altmetric lists. Supporting and contrasting citations were found using Scite.ai database. Articles in both lists, published between 1975-2021, were analyzed in terms of study type, topic, first author, publication year, citation count, AAS, scite score, supporting and contrasting citation counts. Impact factor (IF), quartile of journal and journal citation indicator (JCI) were also examined. Results: A search of "Palliative care" in WoS yielded a total of 50.674 articles. A significant correlation was found between AAS and citation counts (p=0.001, r=0.328) in T100 citation list, and AAS and contrasting citations in T100 altmetric list (p=0,024, r=0,225). There was no statistically significant difference between IF, JCI and Q categories in both lists. Topic "PC for non-oncological diseases" were at the top of both lists. The USA, UK and Canada were countries with the most articles in T100 citation list. Conclusions: Palliative care articles that attract the attention of the academia also resonate on social media. Since AAS can be manipulated, it would be beneficial to use altmetric analysis in combination with bibliometric analysis rather than alone to formulate new policies on palliative care.
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 | 0.016 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.148 | 0.211 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".