Global research trends on the links between primary health care and diabetes from 1980 to 2024: a machine learning-based science mapping
Bibliographic record
Abstract
Purpose: A systematic and detailed examination of studies on diabetes can guide the evaluation of healthcare services, the identification of issues, and the implementation of preventive interventions. Therefore, this study aims to assess diabetes research in the primary health care (PHC) field using machine learning-based bibliometric methods. Materials and methods: In this study, articles related to diabetes in the PHC field were obtained from the Web of Science Core Collection on March 15, 2024. These articles were analyzed using bibliometric methods and the Latent Dirichlet Allocation (LDA) topic modeling technique. Results: The analysis of the studies revealed that 3.355 articles on diabetes in the PHC field were produced by researchers from 114 different countries, 4.226 institutions, and 12.426 individual researchers. Recent years have shown a growing interest in topics such as obesity, hypertension, chronic diseases, exercise, and physical activity within the PHC field. Topic modeling identified eight distinct latent topic clusters: (1) Diabetes management in PHC, (2) Risk factors and management of diabetes in PHC, (3) Acute and chronic complications of diabetes in PHC, (4) Gestational diabetes, (5) Prediabetes and Type 1 diabetes, (6) COVID-19 and diabetes, (7) Quality of life, awareness, and health education, (8) Current treatment methods and guideline resources. Conclusion: Primary Care Diabetes and Journal of Family Medicine and Primary Care are the leading journals in PHC-related diabetes research. The studies show a significant overlap between diabetes research and topics such as hypertension and obesity. Future studies in the PHC field are recommended to focus on diabetic retinopathy and diabetic wound research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".