Impact of COVID-19 on Primary Health Care Research Trends and Suggestions for Better Services Approaches Via Blockchain Based Applications
Bibliographic record
Abstract
Objective: The authors assessed how research in primary healthcare was affected by the COVID-19 pandemic and identified the potential of blockchain technology to address pandemic-related challenges. Methods: This quantitative bibliometric research study used machine learning techniques. A comprehensive analysis of all primary healthcare (PHC) research was conducted using bibliometric data from the WOs. We examined co-authorship, co-occurrences, citation and co-citation, thematic mapping, factorial, document, and Latent Dirichlet Allocationtopic analyses. Our main dataset was 1,885 articles produced by 9,185 researchers from 3,132 institutions in 113 countries. Results: The most cited studies in the PHC field during the pandemic related to telemedicine and remote consultation, along with clinical conditions such as mental health, diabetes, vaccinations, risks during pregnancy, and healthcare of the elderly. In addition, the impact of COVID-19 on educational outcomes, changes to the organization of care, experiences and challenges to PHC physicians and other health professionals, and the diversity of COVID-19 symptoms were prominent. Conclusions: The PHC researchers adapted quickly to the pandemic and conducted multidisciplinary research that helped to mitigate the impact on individuals, health systems, and society. Within this context, blockchain technology can be used to facilitate the security of health data, resource management (e.g., monitoring of the vaccine supply chain), and global collaboration toward pandemic control. By providing transparency, security, and efficiency in these areas, blockchain technology might lead to more effective pandemic preparedness and management in the future.
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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 | 0.105 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.052 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".