A survey of scientific production and collaboration rate among of medical library and information sciences in ISI, scopus and Pubmed databases during 2001-2010
Bibliographic record
Abstract
BACKGROUND: Research is essential for development. In other words, scientific development of each country can be evaluated by researchers' scientific production. Understanding and assessing the activities of researchers for planning and policy making is essential. The significance of collaboration in the production of scientific publications in today's complex world where technology is everything is very apparent. Scientists realized that in order to get their work wildly used and cited to by experts, they must collaborate. The collaboration among researchers results in the development of scientific knowledge and hence, attainment of wider information. The main objective of this research is to survey scientific production and collaboration rate in philosophy and theoretical bases of medical library and information sciences in ISI, SCOPUS, and Pubmed databases during 2001-2010. MATERIALS AND METHODS: This is a descriptive survey and scientometrics methods were used for this research. Then data gathered via check list and analyzed by the SPSS software. Collaboration rate was calculated according to the formula. RESULTS: Among the 294 related abstracts about philosophy, and theoretical bases of medical library and information science in ISI, SCOPUS, and Pubmed databases during 2001-2010, the year 2007 with 45 articles has the most and the year 2003 with 16 articles has the least number of related collaborative articles in this scope. "B. Hjorland" with eight collaborative articles had the most one among Library and Information Sciences (LIS) professionals in ISI, SCOPUS, and Pubmed. Journal of Documentation with 29 articles and 12 collaborative articles had the most related articles. Medical library and information science challenges with 150 articles had first place in number of articles. Results also show that the most elaborative country in terms of collaboration point of view and number of articles was US. "University of Washington" and "University Western Ontario" are the most elaborative affiliation from a collaboration point. CONCLUSION: The average collaboration rate between researchers in this field during the years studied is 0.25. The most completive reviewed articles are single authors that included 60.54% of the whole articles. Only 30.46% of articles were provided with two or more than two authors.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".