ArticleClinical and translational science2012
An initial log analysis of usage patterns on a research networking system.
Article in Clinical and translational science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Research networking and the role of the medical librarian.Journal of the Medical Library Association : JMLA · 2024Article
- Development of standard indicators to assess use of electronic health record systems implemented in low-and medium-income countries.PloS one · 2021Article
- Finding collaborators: toward interactive discovery tools for research network systems.Journal of medical Internet research · 2014Article
- The use and significance of a research networking system.Journal of medical Internet research · 2014Article
- Temporal analysis of the usage log of a research networking system.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Usage data for research networking systems (RNSs) are valuable but generally unavailable for understanding scientific professionals' information needs and online collaborator seeking behaviors. This study contributes a method for evaluating RNSs and initial usage knowledge of one RNS obtained from using this method. We designed a log for an institutional RNS, defined categories of users and tasks, and analyzed correlations between usage patterns and user and query types. Our results show that scientific professionals spend more time performing deep Web searching on RNSs than generic Google users and we also show that retrieving scientist profiles is faster on an RNS than on Google (3.5 seconds vs. 34.2 seconds) whereas organization-specific browsing on a RNS takes longer than on Google (117.0 seconds vs. 34.2 seconds). Usage patterns vary by user role, e.g., faculty performed more informational queries than administrators, which implies role-specific user support is needed for RNSs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.