Contribution of Health Researches in National Knowledge Production: A Scientometrics Study on 15-Year Research Products of Iran
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Researchers, practitioners, and policymakers call for updated valid evidence to monitor, prevent, and control of alarming trends of health problems. To respond to these needs, health researches provide the vast multidisciplinary scientific fields. We quantify the national trends of health research outputs and its contribution in total science products. METHODS: We systematically searched Scopus database with the most coverage in health and biomedicine discipline as the only sources for multidisciplinary citation reports, for all total and health-related publications, from 2000 to 2014. These scientometrics analyses covered the trends of main index of scientific products, citations, and collaborative papers. We also provided information on top institutions, journals, and collaborative research centers in the fields of health researches. RESULTS: In Iran, over a 15-year period, 237,056 scientific papers have been published, of which 81,867 (34.53%) were assigned to health-related fields. Pearson's Chi-square test showed significant time trends between published papers and their citations. Tehran University of Medical Sciences was responsible for 21.87% of knowledge productions share. The second and the third ranks with 11.15% and 7.28% belonged to Azad University and Shahid Beheshti University of Medical Sciences, respectively. In total fields, Iran had the most collaborative papers with the USA (4.17%), the UK (2.41%), and Canada (0.02%). In health-related papers, similar patterns of collaboration followed by 4.75%, 2.77%, and 1.93% of papers. CONCLUSIONS: Despite the ascending trends in health research outputs, more efforts required for the promotion of collaborative outputs that cause synergy of resources and the use of practical results. These analyses also could be useful for better planning and management of planning and conducting studies in these fields.
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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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it