Mary McNamara

1.5k total citations
50 papers, 1.2k citations indexed

About

Mary McNamara is a scholar working on Experimental and Cognitive Psychology, Polymers and Plastics and Electrical and Electronic Engineering. According to data from OpenAlex, Mary McNamara has authored 50 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Experimental and Cognitive Psychology, 9 papers in Polymers and Plastics and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Mary McNamara's work include Organic Electronics and Photovoltaics (9 papers), Conducting polymers and applications (9 papers) and Mental Health Research Topics (8 papers). Mary McNamara is often cited by papers focused on Organic Electronics and Photovoltaics (9 papers), Conducting polymers and applications (9 papers) and Mental Health Research Topics (8 papers). Mary McNamara collaborates with scholars based in United States, Ireland and Australia. Mary McNamara's co-authors include Ronald Klein, Hugh J. Byrne, Nicholas Witt, Tien Yin Wong, Alun D. Hughes, Nish Chaturvedi, Simon Thom, Richard Evans, Orla Howe and Malachy McCann and has published in prestigious journals such as Nano Letters, PLoS ONE and The Journal of Physical Chemistry B.

In The Last Decade

Mary McNamara

47 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mary McNamara United States 17 260 245 215 212 210 50 1.2k
Michael E. Wilson United States 14 79 0.3× 160 0.7× 512 2.4× 64 0.3× 48 0.2× 18 1.9k
Michelle M. Smith United States 26 247 0.9× 65 0.3× 225 1.0× 232 1.1× 14 0.1× 70 2.3k
Mengyuan Zhang China 23 97 0.4× 35 0.1× 261 1.2× 263 1.2× 34 0.2× 134 2.0k
Hidetoshi Iwamoto Japan 21 38 0.1× 91 0.4× 300 1.4× 56 0.3× 67 0.3× 90 2.1k
Jean Kim United States 22 212 0.8× 39 0.2× 333 1.5× 1.5k 6.9× 22 0.1× 55 3.3k
Richard L. Elliott United States 26 114 0.4× 66 0.3× 837 3.9× 143 0.7× 33 0.2× 132 2.2k
Cheng Cao China 24 40 0.2× 56 0.2× 317 1.5× 337 1.6× 12 0.1× 85 1.8k
Ajay Kumar India 19 94 0.4× 55 0.2× 128 0.6× 243 1.1× 55 0.3× 73 1.1k
Faisal Rashid Pakistan 15 155 0.6× 70 0.3× 182 0.8× 80 0.4× 15 0.1× 53 918
Ye Ji Kim South Korea 20 34 0.1× 25 0.1× 122 0.6× 105 0.5× 22 0.1× 64 980

Countries citing papers authored by Mary McNamara

Since Specialization
Citations

This map shows the geographic impact of Mary McNamara'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 Mary McNamara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mary McNamara more than expected).

Fields of papers citing papers by Mary McNamara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary McNamara. 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 Mary McNamara. The network helps show where Mary McNamara may publish in the future.

Co-authorship network of co-authors of Mary McNamara

This figure shows the co-authorship network connecting the top 25 collaborators of Mary McNamara. A scholar is included among the top collaborators of Mary McNamara 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 Mary McNamara. Mary McNamara is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
McNamara, Mary, Jason Shumake, & Christopher G. Beevers. (2025). Attention Allocation for Dysphoric Information in Adults with Depression Symptoms Using Eye-tracking and Mouse-tracking. PLoS ONE. 20(4). e0318923–e0318923.
2.
McNamara, Mary & Christopher G. Beevers. (2024). Measurement properties of web-based attentional bias in depression with cursor-directed aperture in online studies.. Translational Issues in Psychological Science. 10(3). 346–363. 1 indexed citations
3.
Dainer‐Best, Justin, et al.. (2024). Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach. Cognition & Emotion. 39(4). 829–843. 3 indexed citations
4.
Tseng, Andrew S., Andrew Levihn‐Coon, Mary McNamara, et al.. (2024). Efficacy of traditional and gamified attention bias modification for depression: Study protocol for a randomized controlled trial. Contemporary Clinical Trials. 149. 107797–107797.
5.
McNamara, Mary, et al.. (2023). Beyond Face Value: Assessing the Factor Structure of an Eye-Tracking Based Attention Bias Task. Cognitive Therapy and Research. 47(5). 772–787. 4 indexed citations
6.
McNamara, Mary, Patrick Sweigert, Tarik K. Yuce, et al.. (2023). The effect of frailty and age on outcomes in elective paraesophageal hernia repair. Surgical Endoscopy. 37(12). 9514–9522. 4 indexed citations
7.
Dong, Lu, Nicole B. Gumport, Armando Martínez, et al.. (2022). Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? A randomized controlled trial. Behaviour Research and Therapy. 157. 104167–104167. 14 indexed citations
8.
McNamara, Mary, et al.. (2022). Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions. Behaviour Research and Therapy. 153. 104086–104086. 27 indexed citations
9.
Beal, Eliza W., et al.. (2022). Patient-, Provider-, and System-Level Barriers to Surveillance for Hepatocellular Carcinoma in High-Risk Patients in the USA: a Scoping Review. Journal of Gastrointestinal Cancer. 54(2). 332–356. 10 indexed citations
10.
McNamara, Mary, Jason Shumake, John J. B. Allen, et al.. (2021). Multifactorial prediction of depression diagnosis and symptom dimensions. Psychiatry Research. 298. 113805–113805. 19 indexed citations
11.
Hsu, Kean J., Emily Carl, Bridget K. Freihart, et al.. (2021). Rising to the Occasion of This COVID-19-Impacted Nation: Development, Implementation, and Feasibility of the Brief Assessment-Informed Skills Intervention for COVID-19 (BASIC). Cognitive and Behavioral Practice. 28(4). 468–480. 2 indexed citations
12.
Gumport, Nicole B., et al.. (2019). Therapist perceptions of client memory for psychological treatment contents and use of memory support strategies: A survey study of clinical practice.. Professional Psychology Research and Practice. 50(5). 288–295. 5 indexed citations
13.
Harvey, Allison G., Lu Dong, Jason Y. Lee, et al.. (2017). Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? Study protocol for a randomized controlled trial. Trials. 18(1). 539–539. 14 indexed citations
14.
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16.
O’Neill, Luke, et al.. (2006). Spectroscopic Characterization of Novel Polycyclic Aromatic Polymers. The Journal of Physical Chemistry A. 111(2). 299–305. 7 indexed citations
17.
Casey, Alan, et al.. (2005). Interaction of Carbon Nanotubes with Sugar Complexes. Synthetic Metals. 153(1-3). 357–360. 26 indexed citations
18.
Ruether, Manuel, et al.. (2005). Temperature Dependent Spectroscopic studies of HiPco SWNT composites.. Synthetic Metals. 154(1-3). 197–200. 1 indexed citations
19.
McNamara, Mary. (1991). Psychological Factors Affecting Neurological Conditions. Psychosomatics. 32(3). 255–267. 13 indexed citations
20.
McNamara, Mary, et al.. (1989). FT-IR and raman spectral evidence for metal complex formation with?-cyclodextrin as a first sphere ligand. Journal of Inclusion Phenomena and Macrocyclic Chemistry. 7(4). 455–460. 29 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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