Evidence map›Paper›PMID 41064794›Full record

ArticleFrontiers in digital health2025

Self-help psychological intervention for young individuals during the post-COVID-19 era: development of a PST chatbot using GPT-4.

Liuling Mo, He Li, Yanbo Zhang, Ang Li, Ziyue Xiong, Peixin Cun, Tingshao Zhu

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Liuling MoDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
He LiDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
Yanbo ZhangDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
Ang LiDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
Ziyue XiongDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
Peixin CunDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.
Tingshao ZhuDepartment of Social Psychology, School of Sociology, Nankai University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The COVID-19 pandemic has exacerbated psychological stress among young people, with some survivors experiencing persistent mental distress, thus creating an urgent need for accessible psychological intervention tools. To help young people affected by COVID-19 recover and achieve balanced mental health in the post-pandemic era, this study developed an online self-help psychological intervention chatbot to supplement existing mental health resources. Methods: We utilized prompt engineering techniques to construct a chatbot proficient in Problem-Solving Therapy (PST) based on the large language model GPT-4. Subsequently, 7 master's students majoring in psychological counseling were recruited for a pre-test of the chatbot, and 100 young people who had contracted COVID-19 were selected for a formal user experiment to evaluate its effectiveness. Results: The pre-test results indicated that the chatbot followed the core steps of PST during interactions with users and was helpful in problem-solving. The formal experiment showed that the experimental group scored significantly higher than the control group in the dimensions of problem awareness [ Conclusions: These findings support the application of the PST chatbot in post-COVID-19 era psychological interventions, particularly in assisting users with identifying problems and exploring solutions. Although the chatbot did not achieve significant improvement in human-computer relationship quality, its general acceptability and broad applicability demonstrate great potential in the field of mental health, highlighting the value of large language models in promoting self-help mental health interventions as a supplementary tool to existing resources.

Indexed as

chatbotGPT-4post-COVID-19 eraproblem-solving therapyself-help psychological interventionyoung individuals

Identifiers

PMID41064794
PMCPMC12500695

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.