Caitrin Armstrong

508 total citations
10 papers, 99 citations indexed

About

Caitrin Armstrong is a scholar working on Experimental and Cognitive Psychology, Pharmacology and Artificial Intelligence. According to data from OpenAlex, Caitrin Armstrong has authored 10 papers receiving a total of 99 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Experimental and Cognitive Psychology, 4 papers in Pharmacology and 4 papers in Artificial Intelligence. Recurrent topics in Caitrin Armstrong's work include Mental Health Research Topics (5 papers), Treatment of Major Depression (4 papers) and Machine Learning in Healthcare (2 papers). Caitrin Armstrong is often cited by papers focused on Mental Health Research Topics (5 papers), Treatment of Major Depression (4 papers) and Machine Learning in Healthcare (2 papers). Caitrin Armstrong collaborates with scholars based in Canada, United States and Israel. Caitrin Armstrong's co-authors include Derek Ruths, David Jurgens, David Benrimoh, Robert Fratila, Jad Sassine, Kelly Perlman, Sonia Israel, Gustavo Turecki, Ate Poorthuis and Thomas Soehl and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and American Journal of Geriatric Psychiatry.

In The Last Decade

Caitrin Armstrong

9 papers receiving 98 citations

Peers

Caitrin Armstrong

AI

SPS

ECP

COMMU

CN

Nirmal Varghese Babu India

Carina Prunkl United Kingdom

Esteban A. Ríssola Switzerland

Salvador Climent Spain

Gabriel Grand United States

Louise‐Amélie Cougnon Belgium

Alesia Gainer United States

Rebecca Marvin United States

Brendan Spillane Ireland

Assaf Toledo Netherlands

Nirmal Varghese Babu India

Caitrin Armstrong

124 ×3.3

AI

31 ×1.1

SPS

23 ×1.0

ECP

8 ×0.4

COMMU

6 ×0.4

CN

Citations per year, relative to Caitrin Armstrong Caitrin Armstrong (= 1×) peers Nirmal Varghese Babu

Countries citing papers authored by Caitrin Armstrong

Since Specialization

Citations

This map shows the geographic impact of Caitrin Armstrong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Caitrin Armstrong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caitrin Armstrong more than expected).

Fields of papers citing papers by Caitrin Armstrong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caitrin Armstrong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Caitrin Armstrong. The network helps show where Caitrin Armstrong may publish in the future.

Co-authorship network of co-authors of Caitrin Armstrong

This figure shows the co-authorship network connecting the top 25 collaborators of Caitrin Armstrong. A scholar is included among the top collaborators of Caitrin Armstrong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Caitrin Armstrong. Caitrin Armstrong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Golden, Grace, Sonia Israel, Kelly Perlman, et al.. (2024). Applying artificial intelligence to clinical decision support in mental health: What have we learned?. Health Policy and Technology. 13(2). 100844–100844. 17 indexed citations

2.

Perlman, Kelly, David Benrimoh, Caitrin Armstrong, et al.. (2024). Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets. Translational Psychiatry. 14(1). 263–263. 6 indexed citations

3.

Benrimoh, David, Toshi A. Furukawa, Charles F. Reynolds, et al.. (2023). Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies. American Journal of Geriatric Psychiatry. 32(3). 280–292. 5 indexed citations

4.

Rosenfeld, Ariel, David Benrimoh, Robert Fratila, et al.. (2021). Treatment selection using prototyping in latent-space with application to depression treatment. PLoS ONE. 16(11). e0258400–e0258400. 8 indexed citations

5.

Jurgens, David, et al.. (2021). User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest. Proceedings of the International AAAI Conference on Web and Social Media. 10(1). 279–288. 32 indexed citations

6.

Armstrong, Caitrin, Ate Poorthuis, Matthew Zook, Derek Ruths, & Thomas Soehl. (2021). Challenges when identifying migration from geo-located Twitter data. EPJ Data Science. 10(1). 10 indexed citations

7.

Jurgens, David, et al.. (2021). Everyone's Invited: A New Paradigm for Evaluation on Non-Transferable Datasets. Proceedings of the International AAAI Conference on Web and Social Media. 9(4). 8–17.

8.

Fratila, Robert, David Benrimoh, Adam Kapelner, et al.. (2020). Differential Treatment Benet Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data. SHILAP Revista de lepidopterología. 4(0). 61–61. 12 indexed citations

9.

Bhandari, Abhinav & Caitrin Armstrong. (2019). Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities. 57–67. 3 indexed citations

10.

Gagné, Christina L., et al.. (2018). Pseudo-morphemic structure inhibits, but morphemic structure facilitates, processing of a repeated free morpheme. Language Cognition and Neuroscience. 33(10). 1252–1274. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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