Evidence map›Paper›PMID 36312061›Full record

ArticleFrontiers in psychology2022

The influence of ideological education on students' mental health during the pandemic: An empirical analysis based on big data and intelligent model.

Lianxiang Liu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

1 author.

Lianxiang LiuSchool of Marxism, Nanjing University of Science and Technology, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ideological education is an important part of students' education. Good ideological education can greatly reduce students' mental health problems. Based on relevant theories of psychology, this study analyzes how psychological crises can be warned against through continuous observation of emotions. Further, a psychological crisis warning model is built based on education big data, providing innovative observation methods and ideas for warning college students about psychological crises. The core idea of the model is that stress events are the external cause and personality and mood changes are the internal causes. The calculation, based on the evaluation of stress events and personality, can draw on different types of emotions and emotional threshold intensities to judge emotions. At the same time, the evaluation is based on time sequences of mood changes to judge the psychological crises that college students face by the level of risk. Combining psychological knowledge and machine learning methods, this study proposes a psychological crisis warning algorithm based on educational data, which predicts the duration and intensity of emotions by calculating stressful events and emotional attenuation. The simulation results show that the proposed algorithm can reflect the emotional changes of college students when they are subjected to stress events, and the effectiveness of the proposed algorithm is preliminarily verified. We conducted timely psychological intervention for the students who received negative stimuli, and the results show that timely psychological intervention and ideological education support are necessary and helpful.

Indexed as

emotional thresholdideological educationmachine learningpsychological crisispsychological interventionstudents’ mental health

Identifiers

PMID36312061
PMCPMC9599407

What OpenQuestion holds

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LicenceCC BY
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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.