Evidence map›Paper›PMID 35283615›Full record

ArticleCurrent psychology (New Brunswick, N.J.)2022

The relationship between adolescent risk perception and emotions during the COVID-19: a short-term longitudinal study.

Tong-Tong Xin, Xiu-Jun Li, Lei-Shen, Wen-Yu Ding, Wei-Liu, Ning-Chen

Abstract read
In one paragraph

Article in Current psychology (New Brunswick, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 2 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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

6 authors.

Tong-Tong Xin *Shanghai Normal University, Shanghai, China.
Xiu-Jun Li *Shanghai Normal University, Shanghai, China.
Lei-ShenShanghai Normal University, Shanghai, China.
Wen-Yu DingShanghai Normal University, Shanghai, China.
Wei-LiuShanghai Normal University, Shanghai, China.
Ning-ChenShanghai Normal University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the relationship between adolescents' perceptions of epidemic risk and their emotions through three follow-up surveys during the early stages of the COVID-19 pandemic on February 11th (T1), 18th (T2), and 25th (T3), 2020. Three hundred and four adolescents in different academic stages (junior high middle school, senior high middle school, and university) participated in the online survey, and cross-lag analysis was used to examine the causal relationship between epidemic risk perceptions and positive and negative emotions. The results found that the individual's positive emotions were significantly higher than the negative emotions in T1, T2 and T3. Cross-lag analysis found that for positive emotions, T2 positive emotions could negatively predict T3 epidemic risk perceptions, and T2 epidemic risk perceptions could negatively predict the individual's T3 positive emotions. For negative emotions, risk perceptions at T1 could positively predict negative emotions at T2, and at the same time, negative emotions at T1 could also positively predict epidemic risk perceptions at T2. This indicates that during the early stages of the COVID-19 pandemic, there was a causal relationship between the perceptions of epidemic risk and the emotions of adolescents, and this relationship had high stability among groups of different genders and academic stages.

Indexed as

AdolescentCOVID-19Epidemic risk perceptionNegative emotionPositive emotion

Identifiers

PMID35283615
PMCPMC8896418

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.