Evidence map›Paper›PMID 39465346›Full record

ArticleScientific reports2024

The impact of pandemic threats on intertemporal choices in Chinese and Americans.

Xu Han, Yuwei Wang, Yan Mu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xu Han *CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, China.
Yuwei Wang *CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, China.
Yan MuCAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, China. muy@psych.ac.cn.

Funding

CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences 2019000050National Natural Science Foundation of China 32071016
6 · The paper itself

Abstract

Intertemporal decisions are crucial to survival, especially when humans are exposed to ecological threats. However, it remains unclear whether and how pandemic threats impact intertemporal choices across cultures. We conducted two studies in two cultural contexts (N = 1180). Study 1a found that Chinese who perceived more pandemic threats showed higher temporal discounting rates (i.e., preferred immediate smaller over delayed larger rewards), and this relationship was mediated by negative emotions. Study 1b, using threat priming, revealed that Chinese participants primed with a high-threat condition (depicting a pandemic threat) showed higher temporal discounting rates compared to those primed with a low-threat condition (describing the flu). Differently, Study 2a revealed that perceived pandemic threat levels of Americans did not directly predict temporal discounting rates. Using similar threat priming, Study 2b further confirmed that Americans in the high-threat priming condition showed no significant differences in temporal discounting rates compared to those in the low-threat priming condition. The current research deepens the understanding of the cultural difference in the impact of pandemic threats on intertemporal decision-making and further informs the development of interventions that promote individual future-oriented thinking in the face of pandemics.

Indexed as

COVID-19Delay DiscountingPandemicsAdolescentAdultChinaChoice BehaviorDecision MakingEast Asian PeopleFemaleHumansMaleRewardUnited StatesYoung AdultCOVID-19CultureIntertemporal choiceNegative emotionPandemic threat

Identifiers

PMID39465346
PMCPMC11514260

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.