Assessing global, regional, national and sub–national capacity for public health research: a bibliometric analysis of the Web of ScienceTM in 1996–2010
Bibliographic record
Abstract
BACKGROUND: The past two decades have seen a large increase in investment in global public health research. There is a need for increased coordination and accountability, particularly in understanding where funding is being allocated and who has capacity to perform research. In this paper, we aim to assess global, regional, national and sub-national capacity for public health research and how it is changing over time in different parts of the world. METHODS: To allow comparisons of regions, countries and universities/research institutes over time, we relied on Web of Science(TM) database and used Hirsch (h) index based on 5-year-periods (h5). We defined articles relevant to public health research with 98% specificity using the combination of search terms relevant to public health, epidemiology or meta-analysis. Based on those selected papers, we computed h5 for each country of the world and their main universities/research institutes for these 5-year time periods: 1996-2000, 2001-2005 and 2006-2010. We computed h5 with a 3-year-window after each time period, to allow citations from more recent years to accumulate. Among the papers contributing to h5-core, we explored a topic/disease under investigation, "instrument" of health research used (eg, descriptive, discovery, development or delivery research); and universities/research institutes contributing to h5-core. RESULTS: Globally, the majority of public health research has been conducted in North America and Europe, but other regions (particularly Eastern Mediterranean and South-East Asia) are showing greater improvement rate and are rapidly gaining capacity. Moreover, several African nations performed particularly well when their research output is adjusted by their gross domestic product (GDP). In the regions gaining capacity, universities are contributing more substantially to the h-core publications than other research institutions. In all regions of the world, the topics of articles in h-core are shifting from communicable to non-communicable diseases (NCDs). There is also a trend of reduction in "discovery" research and increase in "delivery" research. CONCLUSION: Funding agencies and research policy makers should recognise nations where public health research capacity is increasing. These countries are worthy of increased investment in order to further increase the production of high quality local research and continue to develop their research capacity. Similarly, universities that contribute substantially to national research capacity should be recognised and supported. Biomedical journals should also take notice to ensure equity in peer-review process and provide researchers from all countries an equal opportunity to publish high-quality research and reduce financial barriers to accessing these journals.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.057 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".