Bibliometric Profile of the World Scientific Production on Thanatology in Nursing: Visibility, Impact, and Alternative Metrics
Bibliographic record
Abstract
Objective: We will analyze the bibliometric metrics of the global scientific production in thanatology for nursing care. Methods : A cross-sectional and retrospective study with a bibliometric approach evaluated publications indexed in Scopus from 2017 to 2022. MESH terms were selected, and together with the Boolean operators AND OR a search strategy was developed and applied on December 20, 2022. We also analyzed the metadata of the publications included in the study using Elsevier’s Scival program. Results : Scientific production has increased from 29 in 2017 to 48 in 2022. Most of the articles analyzed had national cooperation (45.2%) and single authorship was presented in a lower proportion (9.6%). The United States (55) is the country with the most publications; in addition, South Korea had the lowest production (10) and lowest weighted impact (FWCI: 0.35). The articles are mainly published in first-quartile journals, such as the Journal of Pain and Symptom Management. The University of Toronto has 71% more citations than expected. Lester David and Dadfar Mahboubeh lead the subject with four published articles each. Conclusion : Thanatology in the field of nursing is a topic that has increased in recent years, and its main means of dissemination are the scientific journals of the Q1 and Q2 quartile. The leading country in this area was the United States, while Brazil was the only Latin American country with institutions among the most productive.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.178 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".