Bibliographic record
Abstract
Aim: To conduct a bibliometric analysis of the nurses’ job satisfaction from 2004 to 2023. Design: The bibliometric and visual analysis was performed in January 2024. Methods: Bibliometric approaches were applied to analyse 11,993 articles, utilising R and VOSviewer software. Results: Articles published by 24,155 authors from 1735 distinct sources between 2004 and 2023 were retrieved from the Web of Science and incorporated into the research’s purview. The most productive nation and institution correspondingly were the United States and the University of Toronto. The leading scholars in this sphere were Spence Laschinger, Heather K, Labrague, Leodoro J, and Rodwell, John according to Price’s Law, author co‐citation and bibliographic‐coupling network analysis. 14,152 keywords about nurses’ job satisfaction study were discovered in this research. The most common keywords encompassed “job satisfaction,” “nurses,” “burnout,” “turnover,” and “intention” It was also observed that while trend topics like “work engagement” “COVID‐19” and “grit” have gained popularity recently, the most commonly employed trend topics in earlier years included “empirical research report” “longitudinal study,” and “organizational characteristics.” Conclusion: Research on nurses’ job satisfaction remains relatively limited and requires more attention, especially in developing countries. Developed countries, especially the United Kingdom and the United States, are the main contributors to nurse job satisfaction research. In the early days, nurse job satisfaction research mainly focused on the current status and influencing factors of nurse job satisfaction in different medical organizations, nurse groups or departments, while more researchers have recently paid more attention to research on specific issues emerging in this field, such as the impact of COVID‐19 on nurse job satisfaction and turnover. In addition, scholars in the field of nurse job satisfaction focus on finding the real determinants of job satisfaction of adult practicing nurses, such as interpersonal value consistency, human resource management, and the impact of job satisfaction of adult nurses in different medical environments. Topics such as “perseverance,” “COVID‐19” and “work engagement” may be potential focuses for future research. Furthermore, transnational research should be given greater emphasis to investigate whether the major factors and effective interferences of nurses’ job satisfaction differ between cultures and more multicenter as well as big sample studies should be conducted to efficiently improve nurses’ job satisfaction. Impact: This study used bibliometric analysis to examine the most contributing nations, institutions, authors, trend topics, and research focus. Data on the present state of nurses’ job satisfaction research, including its knowledge maps, study emphasis, and thematic trends are few. The findings of this research can lay a strong basis for future research and offer direction. No Patient or Public Contribution: There were no humankind subjects in the bibliometric analysis of published papers.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.159 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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, 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".