Science mapping of COVID-19 contributions in primary health care by OECD countries: A machine learning approach
Bibliographic record
Abstract
Purpose: Our study comprehensively assesses how Canada and Organisation for Economic Co-operation and Development (OECD) countries have supported researchers, research institutes and their scientific productivity in primary health care (PHC), one of the areas most affected by COVID-19. Method: We analyzed research contributions among OECD countries and assessed their scientific productivity during COVID-19 using bibliometric methods and machine learning techniques. Our analysis includes co-authorship networks, funding patterns, co-citation analysis, thematic mapping, factor analysis, and topic modeling through latent Dirichlet allocation. Results: This study analyzes 1061 articles and review papers involving 5765 researchers from OECD countries. PHC systems played a crucial role in the global response to SARS-CoV2 but faced significant challenges. Canada ranks third in PHC research output and forth in COVID-19 research among OECD nations. The findings reveal Canada's strong collaborative ties with countries such as the USA, UK, and Australia. However, disparities in PHC scientific productivity across OECD countries remain, with some nations showing minimal progress. Conclusions: Our study highlights the importance of academic collaboration in addressing pandemic-related crises. The study recommends enhancing international collaboration, led by countries such as Canada, the USA, and the UK, to strengthen PHC systems during global health crises. It is deemed necessary to include experts and academics from the field of PHC in such structures. It also emphasizes the need for academic journals to improve transparency in funding sources through automated extraction of bibliometric data from platforms such as Web of Science and Scopus, which is crucial for shaping future health and education policies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.079 | 0.093 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".