Evidence map›Paper›PMID 36051603›Full record

ReviewWorld journal of psychiatry2022

Influencing factors, prediction and prevention of depression in college students: A literature review.

Xin-Qiao Liu, Yu-Xin Guo, Wen-Jie Zhang, Wen-Juan Gao

Open access · diamondAbstract readReview
In one paragraph

Review in World journal of psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 1 pooled it
23.4field-weighted citation impact, top 1% of its field
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

74 citing papers in PubMed, 1 synthesis or guideline pooled it, 133 citations in OpenAlex.

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14 more citing papers are in PubMed but not listed here.

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

4 authors at 3 institutions in 1 country.

Xin-Qiao LiuSchool of Education, Tianjin University, Tianjin 300350, China. xinqiaoliu@pku.edu.cn.
Yu-Xin GuoSchool of Education, Tianjin University, Tianjin 300350, China.
Wen-Jie ZhangGraduate School of Education, Peking University, Beijing 100871, China.
Wen-Juan GaoInstitute of Higher Education, Beihang University, Beijing 100191, China.
Beihang University · CNPeking University · CNTianjin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The high prevalence of depression among college students has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication. This paper reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for college students' depression. The influencing factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of coronavirus disease 2019 has exacerbated the severity of depression among college students worldwide and poses grave challenges to the prevention and treatment of depression, given that the coronavirus has spread quickly with high infection rates, and the pandemic has changed the daily routines of college life. To predict and measure mental health, more advanced methods, such as machine algorithms and artificial intelligence, have emerged in recent years apart from the traditional commonly used psychological scales. Regarding nonpharmaceutical prevention measures, both general measures and professional measures for the prevention and treatment of college students' depression were examined in this study. Students who experience depressive disorders need family support and personalized interventions at college, which should also be supplemented by professional interventions such as cognitive behavioral therapy and online therapy. Through this literature review, we insist that the technology of identification, prediction, and prevention of depression among college students based on big data platforms will be extensively used in the future. Higher education institutions should understand the potential risk factors related to college students' depression and make more accurate screening and prevention available with the help of advanced technologies.

Indexed as

Artificial intelligenceBig dataDepressionMachine learningPredictionPrevention

Identifiers

PMID36051603
PMCPMC9331452
OpenAlexW4285795657

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.