Automated Author ProfileHakimjavadi, Ramtin
BruyèreUniversity of Ottawa
Hakimjavadi, Ramtin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Additional file 1: Supplemental Methods: a description of the methods for patient and public involvement, clinician-led review of frailty related content on eConsult, and phase 1-2 of developing a frailty identification approach using eConsult. Supplemental Table S1. List of frailty-related terms for each frailty topic. List of frailty-related terms for each of the 17 topics. Supplemental Table S2. Prevalence of eConsult cases stratified by number of frailty-related terms. Number of cases and mean overall word count per eConsult case, stratified by the number of frailty-related terms identified in the complete eConsult communication logs. Supplemental Figure S1. Mean overall word count per eConsult case, stratified by the number of frailty-related terms identified in the complete eConsult communication log. Supplemental Figure S2. Plot of the clinician-provided frailty ratings against the total word count in the eConsult text.
Authors
- Hakimjavadi, Ramtin ;
- Karunananthan, Sathya ;
- Fung, Celeste ;
- Levi, Cheryl ;
- Helmer-Smith, Mary ;
- LaPlante, James ;
- Gazarin, Mohamed ;
- Rahgozar, Arya ;
- Afkham, Amir ;
- Keely, Erin ;
- Liddy, Clare
Additional file 1: Supplemental Methods: a description of the methods for patient and public involvement, clinician-led review of frailty related content on eConsult, and phase 1-2 of developing a frailty identification approach using eConsult. Supplemental Table S1. List of frailty-related terms for each frailty topic. List of frailty-related terms for each of the 17 topics. Supplemental Table S2. Prevalence of eConsult cases stratified by number of frailty-related terms. Number of cases and mean overall word count per eConsult case, stratified by the number of frailty-related terms identified in the complete eConsult communication logs. Supplemental Figure S1. Mean overall word count per eConsult case, stratified by the number of frailty-related terms identified in the complete eConsult communication log. Supplemental Figure S2. Plot of the clinician-provided frailty ratings against the total word count in the eConsult text.
Authors
- Hakimjavadi, Ramtin ;
- Karunananthan, Sathya ;
- Fung, Celeste ;
- Levi, Cheryl ;
- Helmer-Smith, Mary ;
- LaPlante, James ;
- Gazarin, Mohamed ;
- Rahgozar, Arya ;
- Afkham, Amir ;
- Keely, Erin ;
- Liddy, Clare