A Review of Published Scientific Outputs in the Field of Budgeting Methods in the Health System
Bibliographic record
Abstract
Background and Aim: Budgeting in the health system plays a crucial role in enhancing the quality of healthcare services, increasing equitable access to health care, and reducing costs. Financial decision-making based on scientific data and evidence can improve the efficiency of the health system and ensure equity in resource allocation. This study aimed to examine the scientific status and trends of published literature on budgeting methods in the health system using bibliometric analysis to assist policymakers in making better financial decisions. Materials and Methods: This study is a bibliometric review with a descriptive–analytical approach, analyzing 222 scientific documents indexed in Scopus between 1974 and 2024. Data were analyzed using Excel, Bibexcel, VOSviewer, and Gephi software to map the knowledge structure, co-word relationships, and international collaborations in this field. Results: The United States (30%), Taiwan (15%), and Canada (10%) were the leading contributors to scientific output in this field. Journal articles comprised nearly 90% of all publications. The most frequent keywords were “budget,” “health care cost,” and “financial management,” reflecting a strong emphasis on cost control and resource management. Three main budgeting approaches were identified: performance-based, traditional (historical), and needs-based. Traditional budgeting remains dominant in developing countries, particularly where information infrastructure and managerial capacity are limited. International collaboration involved 18 countries, with the strongest cooperation observed between the United States and Taiwan. Conclusion: Improving the health budgeting system requires strengthening information systems, training managers, and enhancing international scientific collaboration. Resource allocation based on scientific data and bibliometric insights can optimize resource distribution and enhance equity in access to health services. Such measures would lead to greater health system efficiency and more comprehensive financial decision-making.
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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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.058 | 0.088 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".