Research in Integrated Health Care and Publication Trends from the Perspective of Global Informatics
Bibliographic record
Abstract
BACKGROUND: Integrated care has gained popularity in recent decades and is advocated by the World Health Organization. This study examined the global progress, current foci, and the future of integrated care. METHODS: We conducted a scientometric analysis of data exported from the Web of Science database. Publication number and citations, co-authorship between countries and institutions and cluster analysis were calculated and clustered using Histcite12.03.07 and VOS viewer1.6.4. RESULTS: We retrieved 6127 articles from 1997 to 2016. We found the following. (1) The United States, United Kingdom, and Canada had the most publications, citations, and productive institutions. (2) The top 10 cited papers and journals were crucial for knowledge distribution. (3) The 50 author keywords were clustered into 6 groups: digital medicine and e-health, community health and chronic disease management, primary health care and mental health, healthcare system for infectious diseases, healthcare reform and qualitative research, and social care and health policy services. CONCLUSIONS: This paper confirmed that integrated care is undergoing rapid development: more categories are involved and collaborative networks are being established. Various research foci have formed, such as economic incentive mechanisms for integration, e-health data mining, and quantitative studies. There is an urgent need to develop performance measurements for policies and models.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.092 | 0.196 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".