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Record W3178181480 · doi:10.3389/fpsyg.2021.636062

Cultural Differences in People’s Psychological Response to COVID-19

2021· article· en· W3178181480 on OpenAlexafffundabout
Suhui Yap, Albert Lee, Li‐Jun Ji, Ye Li, Ying Dong

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsQueen's University
FundersMinistry of Education, IndiaQueen's University
KeywordsOptimismPsychologyAffect (linguistics)Meaning (existential)Social psychologyCoronavirus disease 2019 (COVID-19)Cultural diversityWell-being2019-20 coronavirus outbreakDevelopmental psychologyPsychotherapistSociologyMedicine

Abstract

fetched live from OpenAlex

The present research studied Chinese and Euro-Canadian students during the COVID-19 pandemic, focusing on their affect, optimism, well-being, and meaning in life. The results revealed both differences and similarities across cultures. As predicted, Chinese participants reported more positive affect and less negative affect, higher optimism, higher state psychological well-being, and higher meaning presence, compared to Euro-Canadian participants. The findings were replicated after a week's delay. Analyses on longitudinal data showed that state optimism, state well-being, and meaning presence influenced one another over time. These variables also mediated the cultural differences in one another. These results are consistent with cultural work on naïve dialecticism and non-linear lay theory of change. Results also demonstrate underlying relationships among the constructs that are common to both cultural groups. Broadly, the present research highlights the impact of culture on people's response to challenging life situations and the mechanisms underlying these cultural differences.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.475
Teacher spread0.370 · 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

Citations27
Published2021
Admission routes3
Has abstractyes

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