ArticleInternational journal of environmental research and public health2022
Human Personality Is Associated with Geographical Environment in Mainland China.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Does a Small Country Have Meaningful Regional Personality Differences? The Case of Estonia.Journal of personality · 2026Article
- The Myers-Briggs type indicator association with United States medical student performance, demographics, and career values.BMC medical education · 2024Article
- The lay of the land: Associations between environmental features and personality.Journal of personality · 2024Article
- ecolo-zip: A global, rich and granular characterization of biogeophysical ecology for 1.5 million postal codes.Scientific data · 2023Article
- The self-medication behaviors of residents and the factors related to the consideration of drug efficacy and safety-A cross-sectional study in China.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent psychological research shown that the places where we live are linked to our personality traits. Geographical aggregation of personalities has been observed in many individualistic nations; notably, the mountainousness is an essential component in understanding regional variances in personality. Could mountainousness therefore also explain the clustering of personality-types in collectivist countries like China? Using a nationwide survey (29,838 participants) in Mainland China, we investigated the relationship between the Big Five personality traits and mountainousness indicators at the provincial level. Multilevel modelling showed significant negative associations between the elevation coefficient of variation (
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