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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Pilot Study Using Frequent Inpatient Assessments of Suicidal Thinking to Predict Short-Term Postdischarge Suicidal Behavior
Shirley B. Wang, Daniel D.L. Coppersmith, Evan M. Kleiman, et al.
JAMA Network Open (2021) Vol. 4, Iss. 3, pp. e210591-e210591
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records
Matthew K. Nock, Alexander J. Millner, Eric L. Ross, et al.
JAMA Network Open (2022) Vol. 5, Iss. 1, pp. e2144373-e2144373
Open Access | Times Cited: 109
Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research
Liia Kivelä, Willem van der Does, Harriëtte Riese, et al.
Frontiers in Digital Health (2022) Vol. 4
Open Access | Times Cited: 79
Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions
Daniel D.L. Coppersmith, Walter Dempsey, Evan M. Kleiman, et al.
Psychiatry (2022) Vol. 85, Iss. 4, pp. 317-333
Open Access | Times Cited: 77
Mapping the timescale of suicidal thinking
Daniel D.L. Coppersmith, Oisín Ryan, Rebecca G. Fortgang, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 17
Open Access | Times Cited: 53
Translating promise into practice: a review of machine learning in suicide research and prevention
Olivia J Kirtley, Kasper van Mens, Mark Hoogendoorn, et al.
The Lancet Psychiatry (2022) Vol. 9, Iss. 3, pp. 243-252
Open Access | Times Cited: 59
The use of advanced technology and statistical methods to predict and prevent suicide
Evan M. Kleiman, Catherine R. Glenn, Richard T. Liu
Nature Reviews Psychology (2023) Vol. 2, Iss. 6, pp. 347-359
Open Access | Times Cited: 29
The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review
Karen Kusuma, Mark Larsen, Juan C. Quiroz, et al.
Journal of Psychiatric Research (2022) Vol. 155, pp. 579-588
Closed Access | Times Cited: 35
Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery
Jacob K. Greenberg, Madelyn Frumkin, Ziqi Xu, et al.
Neurosurgery (2024)
Closed Access | Times Cited: 7
Acceptability and Feasibility of a Smartphone-Based Real-Time Assessment of Suicide Among Black Men: Mixed Methods Pilot Study
Leslie B. Adams, Thomasina Watts, Aubrey DeVinney, et al.
JMIR Formative Research (2024) Vol. 8, pp. e48992-e48992
Open Access | Times Cited: 6
Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization
Ewa K. Czyz, Hyun Jung Koo, Nadia Al‐Dajani, et al.
Psychological Medicine (2021) Vol. 53, Iss. 7, pp. 2982-2991
Open Access | Times Cited: 36
Improving Suicide Prevention in Primary Care for Differing Levels of Behavioral Health Integration: A Review
Margaret Spottswood, Christopher T. Lim, Dimitry S. Davydow, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 26
Brief Interventions for Self-injurious Thoughts and Behaviors in Young People: A Systematic Review
Mallory Dobias, Sharon Chen, Kathryn R. Fox, et al.
Clinical Child and Family Psychology Review (2023) Vol. 26, Iss. 2, pp. 482-568
Open Access | Times Cited: 15
Rapid intensification of suicide risk preceding suicidal behavior among primary care patients
Craig J. Bryan, Michael H. Allen, Heather M. Wastler, et al.
Suicide and Life-Threatening Behavior (2023) Vol. 53, Iss. 3, pp. 352-361
Open Access | Times Cited: 14
Heterogeneity in suicide risk: Evidence from personalized dynamic models
Daniel D.L. Coppersmith, Evan M. Kleiman, Alexander J. Millner, et al.
Behaviour Research and Therapy (2024) Vol. 180, pp. 104574-104574
Closed Access | Times Cited: 5
Training the Next Generation of Clinical Psychological Scientists: A Data-Driven Call to Action
Dylan G. Gee, Kathryn A. DeYoung, Katie A. McLaughlin, et al.
Annual Review of Clinical Psychology (2022) Vol. 18, Iss. 1, pp. 43-70
Open Access | Times Cited: 20
Temporal profiles of suicidal thoughts in daily life: Results from two mobile-based monitoring studies with high-risk adolescents
Ewa K. Czyz, Hyun Jung Koo, Nadia Al‐Dajani, et al.
Journal of Psychiatric Research (2022) Vol. 153, pp. 56-63
Open Access | Times Cited: 20
Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals
Lei Chang, Diyang Qu, Kunxu Liu, et al.
JAMA Network Open (2023) Vol. 6, Iss. 9, pp. e2333164-e2333164
Open Access | Times Cited: 11
Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample
Santiago Papini, Honor Hsin, Patricia Kipnis, et al.
JAMA Psychiatry (2024) Vol. 81, Iss. 7, pp. 700-700
Closed Access | Times Cited: 4
Validation of the Multidimensional Assessment of Interoceptive Awareness Scale in a Sample of Transgender and Gender-Diverse Adults
Rachel E. Frietchen, Marley G. Billman Miller, Dominic M. Denning, et al.
Assessment (2025)
Closed Access
Digital Assessments of Psychiatric Disorders
Rachel Quist, Sukanya Bhattacharya, Sarah M. Chacko, et al.
Oxford University Press eBooks (2025), pp. 967-976
Closed Access
Let’s Move Towards Precision Suicidology
Philippe Courtet, Pilar A. Sáiz
Current Psychiatry Reports (2025)
Closed Access
Mathematical and Computational Modeling of Suicide as a Complex Dynamical System
Shirley B. Wang, Donald J. Robinaugh, Alexander J. Millner, et al.
(2023)
Open Access | Times Cited: 10
Embracing Scientific Humility and Complexity: Learning “What Works for Whom” in Youth Psychotherapy Research
Michael C Mullarkey, Jessica L. Schleider
Journal of Clinical Child & Adolescent Psychology (2021) Vol. 50, Iss. 4, pp. 443-449
Open Access | Times Cited: 21
Machine learning to advance the prediction, prevention and treatment of eating disorders
Shirley B. Wang
European Eating Disorders Review (2021) Vol. 29, Iss. 5, pp. 683-691
Open Access | Times Cited: 21
Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial
Lauren N. Forrest, Valentina Ivezaj, Carlos M. Grilo
Psychological Medicine (2021) Vol. 53, Iss. 7, pp. 2777-2788
Open Access | Times Cited: 21
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