The Co-authorship Network and Citation Analysis of Health Knowledge Management Researchers
Bibliographic record
Abstract
The critical role of knowledge management in healthcare is underscored by the adverse effects that can result from the gap between knowledge production and its practical application, particularly for patients. This study aims to evaluate the current state of healthcare knowledge management by conducting a citation and co-authorship analysis of research articles in this domain. Scientific publications indexed in the Scopus database from 2013 to 2023 were examined. VOSviewer software was employed for co-citation, co-authorship, and keyword co-occurrence analyses. In contrast, Excel software evaluated publication output, citation counts, and average citations per article. The findings reveal that 2018–2019 marked the peak period for scientific output in this field. Graham I.D. emerged as the most experienced and productive author. Regarding international collaboration, the United States and Canada demonstrated the highest cooperation and citations among the countries analyzed. These insights offer valuable guidance for policymakers, planners, and researchers in shaping scientific and educational strategies for healthcare knowledge management.
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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 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.065 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".