Mining Liang

2.8k citations
14 papers · 1.9k · 1 hit paper · h-index 10

Impact in

Papers in

Mining Liang

13 papers receiving 1.8k citations

Mining Liang's Hit Papers

Mental health care for medical staff in China during the COVID-19 outbreak 2020 · 1.6k citations
1.6k0+2+4Years since publication50010001.5k

Peers

Mining Liang
Comparison fields: 5 of 113
  • Clinical Psychology 1.1k
  • General Dentistry 37
  • General Health Professions 454
  • Applied Psychology 81
  • Occupational Therapy 56
Replace Jincai Guo with:
Jincai Guo China
Yunyun Fang China
Subham Chatterjee India
Dan Luo China
Wanqiu Tan Singapore
Huiqiao Huang China
Bella Savitsky Israel
Mohammad Muhit Australia
Tuyen Van Duong Taiwan
Mining Liang relative to Jincai Guo China Jincai Guo's profile →
Citations per field
00.5×1.5×
Jincai Guo · 1×
Citations per year

Countries citing papers authored by Mining Liang

Since Specialization
Citations

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

Fields of papers citing papers by Mining Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mining Liang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mining Liang Line = papers co-authored together Mining Liang links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
Mental health care for medical staff in China during the COVID-19 outbreak
Hit paper breakdown →
20201589
2 2016118
3 202048
4 202023
5 201617
6 202117
7 202115
8 202013
9 202312
10 202011
11 20199
12 20215
13 20091
14 20240

About Mining Liang

Mining Liang is a scholar working on Surgery, Social Psychology, Clinical Psychology, Epidemiology and Periodontics, having authored 14 papers that have together received 1.9k indexed citations. Recurring topics across this work include Oral Health Pathology and Treatment (2 papers), COVID-19 and Mental Health (2 papers), Mental Health Treatment and Access (2 papers), Dental Research and COVID-19 (1 paper), Ferroptosis and cancer prognosis (1 paper), Disaster Response and Management (1 paper), Resilience and Mental Health (1 paper) and Cardiac, Anesthesia and Surgical Outcomes (1 paper). The work is most often cited by research in Clinical Psychology (1.1k citations), General Dentistry (37 citations), General Health Professions (454 citations), Applied Psychology (81 citations) and Occupational Therapy (56 citations). Mining Liang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Jincai Guo, Qiongni Chen, Li He, Yamin Li, Yiwen Cai, Xiaojuan Li, Ling Wang, Dongxue Fei, Jianjian Wang and Lezhi Li. Their work appears in journals such as Frontiers in Psychiatry, Medicine, Oncotarget, Journal of American College Health and The Lancet Psychiatry.

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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