Construction and verification of a risk prediction model of psychological distress in psychiatric nurses.

Background: Psychiatric nurses are more likely to experience psychological distress due to various factors in work and life. Establishing an early warning model of psychological distress for psychiatric nurses is helpful for reducing the incidence of psychological distress.

Aims: To explore the influencing factors of psychological distress in psychiatric nurses and construct and verify a risk prediction nomogram model.

Methods: A total of 812 psychiatric nurses were selected from psychiatric hospitals in Shandong Province from August to September 2022. They were divided into a negative group (K10 < 16 points) and a positive group (K10 ≥ 16 points) according to whether they experienced psychological distress. The elements contributing to psychological discomfort in psychiatric nurses were investigated via multivariate logistic regression analysis. R4.2.3 software and the rms program package were used to construct a risk prediction nomogram model for psychiatric nurses' psychological distress. The prediction effect and degree of fit of the nomogram model were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer-Lomoshow goodness-of-fit test.

Results: Logistic regression analysis revealed that five indices, namely, senior nurses' professional title, self-efficacy of psychological capital (PCQ-R), emotional exhaustion (EE) and personal accomplishment (PA) in job burnout (MBI), and the total Pittsburgh Sleep Quality Index (PSQI) score, were independent risk factors for the psychological distress of psychiatric nurses (P < 0.05). The area under the ROC curve (AUC) of the constructed nomogram prediction model was 0.91695% CI (95% CI: 0.891-0.941), the best cutoff value was 0.610, the sensitivity was 89.4%, and the specificity was 81.1%. The results of the calibration curve analysis revealed that the calibration curve of the column graph model for predicting the psychological distress of psychiatric nurses was close to the ideal curve. The Hosmer-Lemeshow goodness-of-fit test revealed no significant difference between the incidence of psychological distress predicted by the column-line model and the actual incidence among psychiatric nurses (x = 8.064, P = 0.472).

Conclusions: The nomogram model, based on the professional title of nurses, the self-efficacy dimension of psychological capital, the emotional exhaustion and personal accomplishment dimension of job burnout, and the total score of the Pittsburgh sleep quality index, can effectively predict the risk of psychological distress in psychiatric nurses.

Trial Registration: All the investigations in this study were authorized by the Shandong Mental Health Center's Ethics Committee [2023] No. (37).