Dataset Information

A novel model to predict mental distress among medical graduate students in China

ABSTRACT:

Background

Poor mental health was reported among medical graduate students in some studies. Identification of risk factors for predicting the mental health is capable of reducing psychological distress among medical graduate students. Therefore, the aim of the study was to identify potential risk factors relating to mental health and further create a novel prediction model to calculate the risk of mental distress among medical graduate students.

Methods

This study collected and analyzed 1079 medical graduate students via an online questionnaire. Included participants were randomly classified into a training group and a validation group. A model was developed in the training group and validation of the model was performed in the validation group. The predictive performance of the model was assessed using the discrimination and calibration.

Results

One thousand and fifteen participants were enrolled and then randomly divided into the training group (n = 508) and the validation group (n = 507). The prevalence of severe mental distress was 14.96% in the training group, and 16.77% in the validation group. The model was developed using the six variables, including the year of study, type of student, daily research time, monthly income, scientific learning style, and feeling of time stress. The area under the receiver operating characteristic curve (AUROC) and calibration slope for the model were 0.70 and 0.90 (95% CI: 0.65 ~ 1.15) in the training group, respectively, and 0.66 and 0.80 (95% CI, 0.51 ~ 1.09) in the validation group, respectively.

Conclusions

The study identified six risk factors for predicting anxiety and depression and successfully created a prediction model. The model may be a useful tool that can identify the mental status among medical graduate students.

Trial registration

No.ChiCTR2000039574, prospectively registered on 1 November 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-021-03573-9.

SUBMITTER: Guo F

PROVIDER: S-EPMC8591601 | biostudies-literature |

REPOSITORIES: biostudies-literature

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