Automated Author ProfileKent, Julie-Ann
Kent, Julie-Ann
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: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Objective: Appropriate normative data are crucial for competent neuropsychological assessment. Although individuals with psychiatric illness often perform more poorly than healthy adults on neuropsychological testing, data that reflect the psychiatric population are often lacking. We present a normative dataset and calculation tools for the Rey–Osterrieth Complex Figure Test (RCFT) derived from the psychiatric inpatient population. Method: A sample of 301 psychiatric inpatients completed the RCFT and the Test of Memory Malingering (TOMM) between 1999 and 2018. Participants were 59.5% male, 82.1% Caucasian, 13.3% black, and 4.6% identified as another racial demographic, largely consistent with recent Substance Abuse and Mental Health Services Administration (2018) data for inpatients in U.S. psychiatric facilities. Scores for RCFT Copy, Short-Delay Free Recall, Long-Delay Free Recall, Total Recognition, and Percent Retained were modeled via multiple regression with age and education as predictors. Base rates were computed for subscores comprising Total Recognition to aid clinical decision making. Results: Age and education served as significant individual predictors for all models except one model predicting percent retained across delay that included only age. Regression equations and regression standard errors were used to produce a score calculator using a commonly available spreadsheet software package. Healthy adult norms under-estimated performance in our sample, underscoring the importance of these normative data. Conclusions: These normative data for the RCFT represent a large cohort of psychiatric inpatients. For clinical practice and research, both the data and the tools provided are likely to be of particular usefulness among individuals with serious mental illness.
Authors
- Lee, Bern G. ;
- Kent, Julie-Ann ;
- Marcopulos, Bernice A. ;
- Arredondo, Beth C. ;
- Wilson, Monique
Objective: Appropriate normative data are crucial for competent neuropsychological assessment. Although individuals with psychiatric illness often perform more poorly than healthy adults on neuropsychological testing, data that reflect the psychiatric population are often lacking. We present a normative dataset and calculation tools for the Rey–Osterrieth Complex Figure Test (RCFT) derived from the psychiatric inpatient population. Method: A sample of 301 psychiatric inpatients completed the RCFT and the Test of Memory Malingering (TOMM) between 1999 and 2018. Participants were 59.5% male, 82.1% Caucasian, 13.3% black, and 4.6% identified as another racial demographic, largely consistent with recent Substance Abuse and Mental Health Services Administration (2018) data for inpatients in U.S. psychiatric facilities. Scores for RCFT Copy, Short-Delay Free Recall, Long-Delay Free Recall, Total Recognition, and Percent Retained were modeled via multiple regression with age and education as predictors. Base rates were computed for subscores comprising Total Recognition to aid clinical decision making. Results: Age and education served as significant individual predictors for all models except one model predicting percent retained across delay that included only age. Regression equations and regression standard errors were used to produce a score calculator using a commonly available spreadsheet software package. Healthy adult norms under-estimated performance in our sample, underscoring the importance of these normative data. Conclusions: These normative data for the RCFT represent a large cohort of psychiatric inpatients. For clinical practice and research, both the data and the tools provided are likely to be of particular usefulness among individuals with serious mental illness.
Authors
- Lee, Bern G. ;
- Kent, Julie-Ann ;
- Marcopulos, Bernice A. ;
- Arredondo, Beth C. ;
- Wilson, Monique