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Record W4410517285 · doi:10.1177/01461672251335364

From Challenge to Confidence: The Collapse of Trust in the Pandemic Era and the Protective Role of Belief in a Just World

2025· article· en· W4410517285 on OpenAlexaff
Yutong Liu, Xiruo Zhang, Shiming Yao, Wen Zhang, Yifan Wang, Jieying Chen, Yan Mu

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2025
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Manitoba
FundersInstitute of Psychology, Chinese Academy of SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsJust-world hypothesisInterpersonal communicationPriming (agriculture)PsychologyPsychological resilienceSocial psychologyPandemicCoronavirus disease 2019 (COVID-19)Social trustPolitical scienceLawSocial capital

Abstract

fetched live from OpenAlex

Trust plays a fundamental role in almost every social domain, especially when society is facing multi-level threats. However, the impact of threats on trust and its underlying mechanism remains poorly examined. To address this gap, we conducted a series of studies with an array of methodologies spanning cross-cultural surveys, longitudinal designs, and experimental manipulations. Compelling evidence demonstrated that threats (particularly pandemics) precipitate a decline in levels of multiple forms of trust from interpersonal to institutional domains. Additionally, drawing on both correlational and causal methods (e.g., longitudinal design and priming manipulation), belief in a just world (BJW) mediates this relationship. The findings lay the groundwork for a universal Threats-BJW-Trust model of trust in times of crisis. This model extends its influence beyond the trust domain and holds profound implications for bolstering societal resilience and overall well-being.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.388
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venuePersonality and Social Psychology BulletinSame topicCultural Differences and ValuesFrench-language works237,207