Automated Author ProfileKoirala, Gobinda
Koirala, Gobinda
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: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
While programmes to combat malnutrition and to lower barriers to healthcare have been developed, some children with severe acute malnutrition do not benefit at the level necessary for their condition. In this context, community health advice could help caregivers provide the required support for their children. This study looked at this issue in Nepal, with the aim of examining which factors lead to relevant health advice from community members. The data was collected through a questionnaire measuring individual variables (beliefs, emotions) and contextual variables (perceived emotions by community members, level of wealth, access to communication, beliefs in traditional healing by the female community health volunteer) carried out in two Nepalese districts: Saptari and Nuwakot. The results show that factors predicting health advice differed in the two districts, and the role of access to communication was even the opposite. The best predictors were not found at an individual level, but at a contextual level (e.g. perceived emotions by community members, access to communication, etc.). These findings suggest that malnutrition would be better tackled by acting at a more global level, i.e. targeting representations associated to malnutrition and circulating at the national and community level.
Authors
- Caillaud, Sabine ;
- Ginguene, Stéphéline ;
- Leroy, Tanguy ;
- Maharian, Sujen Man ;
- Koirala, Gobinda ;
- Le Roch, Karine