A Systematic Review on International Health Policy and Services Research: A Bibliometric Analysis
Bibliographic record
Abstract
Abstract Background: The number of research publications on health policy and services has been strikingly increasing. This study aimed to reveal the current status and trends of global research on health policy and services and provide insights for health policy and services research. Methods: Based on the Social Science Citation Index (SSCI) database, research on health policy and services published between 1999 and 2018 were identified. The published journals, temporal trends, collaboration between countries/territories and research institutions, and hotspots in health policy and services’ research were analyzed using bibliometric analysis. Results: The number of health policy and services publications has been continuously increasing since 1999, especially after 2008. Health Affairs, Medical Care, and Health Services Research were the most active journals in this field. Scholars from the United States (US), the United Kingdom (UK) and Canada have published more related publications than other countries. Further, The US produced the largest number of independent and collaborative articles, and it took a central position in the collaboration network and had the greatest number of prolific institutions. Harvard University was the largest contributor of single-institution and collaborative publications and occupied a key position in collaborative networks. A keywords analysis demonstrated that medicare, quality of care, and primary care have always been researched hotspots in this field. The rankings of implementation, patient safety, and disparities have risen the most, suggesting a new research trend in the field since the 21st century. Conclusions: Our study identified the temporal and spatial characteristics, academic collaborations, and hotspots in scientific research output in the area of health policy and services. It can provide insights for health policy and services research and global health governance.
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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 | 0.050 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.209 | 0.266 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".