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Record W4206997599 · doi:10.1037/xap0000380

Risky but alluring: Severe COVID-19 pandemic influence increases risk taking.

2021· article· en· W4206997599 on OpenAlexaffabout
Claire I. Tsai, Ying Zeng

Bibliographic record

VenueJournal of Experimental Psychology Applied · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsycINFOPandemicBoredomRisk perceptionCoronavirus disease 2019 (COVID-19)PsychologyPsychological interventionPerceptionSocial psychologyDemographyMEDLINEMedicinePolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has changed our lives to a profound extent. In this research, we examined how the pandemic might have influenced people's general risk attitude in their daily lives. Across four studies (two preregistered) using U.S. online worker and Canadian university student samples, we observed that individuals who were severely affected by the pandemic showed higher risk taking toward a variety of risky activities than those who were less severely affected. We attributed this effect to elevated boredom levels and increased perceived benefits from taking risks among the severely affected group and provided supporting evidence. Data ruled out risk perception, income, employment status, and response biases as alternative explanations. Our findings shed light on the psychological consequences of the COVID-19 pandemic, decision under risk, the role of perceived benefits of risk taking, and effective policy interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.008
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.400
Teacher spread0.344 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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