Qi Qiang
Biographic Data
| ID | 7973283 |
|---|---|
| NAME | Qi Qiang |
| GIVEN NAMES | Qi |
| FAMILY NAME | Qiang |
| SIGNATURE | QIANG Q |
| AFFILIATIONS | Liaoning Normal University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Identifying risk factors for depression and positive/negative mood changes in college students using machine learning
Background: In this study, machine learning was used to assess the prediction of the magnitude of depression changes in college students based on various psychological variable information. Methods: A group of college students from a certain school completed two assessments in October 2021 and March 2022, respectively. We collected baseline levels of depression, demographic variables, parenting styles, college students' mental health information,…
Event-related potential evidence of impaired proactive control in individuals with subthreshold depression
These findings suggest proactive control deficits in individuals with SD, as evidenced by diminished attentional allocation to the cue and inefficient cue utilization
Use of machine learning for simplification of University Personality Inventory (UPI)
Rapid diagnosis of mental health problems is crucial for college students. The University Personality Inventory (UPI) is a commonly used tools for assessing mental health in college students; however, it has certain limitations. This study aimed to develop a machine learning model for predicting the simplified UPI items that can rapidly and effectively screen for mental health issues. To construct the dataset, we administered the UPI to 5155 coll…
No prominent works on this page.
Use of machine learning for simplification of University Personality Inventory (UPI)
Rapid diagnosis of mental health problems is crucial for college students. The University Personality Inventory (UPI) is a commonly used tools for assessing mental health in college students; however, it has certain limitations. This study aimed to develop a machine learning model for predicting the simplified UPI items that can rapidly and effectively screen for mental health issues. To construct the dataset, we administered the UPI to 5155 coll…
Identifying risk factors for depression and positive/negative mood changes in college students using machine learning
Background: In this study, machine learning was used to assess the prediction of the magnitude of depression changes in college students based on various psychological variable information. Methods: A group of college students from a certain school completed two assessments in October 2021 and March 2022, respectively. We collected baseline levels of depression, demographic variables, parenting styles, college students' mental health information,…
Event-related potential evidence of impaired proactive control in individuals with subthreshold depression
These findings suggest proactive control deficits in individuals with SD, as evidenced by diminished attentional allocation to the cue and inefficient cue utilization
Psychology (3 works) · Clinical Psychology (2 works) · Digital Mental Health Interventions (2 works) · Medicine (2 works) · Mental Health via Writing (2 works) · Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (1 works) · Applied Psychology (1 works) · Audiology (1 works) · Beck Depression Inventory (1 works) · Clinical Psychology (1 works)