[Study on the relationship between quality of nursing professional life and depression tendency of nursing staff]

Objective: To explore the relationship between the quality of professional life and depression tendency of nurses. Methods: From November 2018 to January 2019, 242 nurses from some third-class A hospitals were randomly selected as the subjects. The Quality of Professional Life Scale of Nurses were used to evaluate the status of nurses' professional life quality, and the depression state of nurses was measured by Self Rating Depression Scale, and the relationship between them was analyzed. Results: The total score of professional life of nurses was (156.86±26.60) , including family work balance (24.60±4.73) , working environment (77.30±14.78) , workload (36.34±6.11) and social environment (18.62±4.21) . The total score of depression tendency of nurses was (43.76±9.50) , the overall depression tendency rate was 64.46% (156/242) , and the incidence rates of moderate and severe depression tendency were 23.55% (57/242) and 10.74% (26/242) , respectively. There was negative correlation between the total score of professional quality of life and each dimension score with depression tendency (P<0.05) ; Multiple linear regression analysis showed that high quality of professional life was the protective factor of depression tendency of nurses (P<0.001) . Conclusion: The quality of professional life of nurses is related to depression tendency, and the high quality of professional life is not prone to depression.

[Association of occupational stress with job burnout and depression tendency in workers in Internet companies]

Objective: To investigate the association of occupational stress with job burnout and depression tendency in workers in Internet companies. Methods: From July to November, 2016, the cross-sectional method was used to perform a questionnaire survey of 3603 workers in 35 Internet companies in Beijing, Shandong Province, and Zhejiang Province in China, and the association of occupational stress with job burnout and depression tendency was analyzed. Results: Among these workers, 63.70% had occupational stress with job demand-control (JDC) and 34.60% had occupational stress with effort-reward imbalance (ERI) ; among the workers engaged in sales, 75.63% had occupational stress with JDC and 62.70% had occupational stress with ERI. Of all workers, 10.69% had job burnout, and among the workers engaged in sales, 22.12% had job burnout. Of all workers, 18.79% had the tendency of moderate-to-severe or severe depression, and among the workers engaged in sales, 46.13% had such tendency. Occupational stress with JDC increased the risk of job burnout and depression (odds ratio[OR]=3.52 and 1.85, P<0.05) , and occupational stress with ERI also increased the risk of job burnout and depression (OR=8.24 and 5.59, P<0.05) . In addition, irregular diet and insomnia were risk factors for job burnout; age ≥41 years, low income, sales position, working time spent on the screen ≥10 hours/day, insomnia, and poor self-evaluated health status were risk factors for depression tendency. Conclusion: Occupational stress with JDC and ERI increases the risk of job burnout and depression tendency, and among the workers in Internet companies, the workers engaged in sales have the most severe occupational stress, job burnout, and depression tendency.

Analyzing depression tendency of web posts using an event-driven depression tendency warning model

OBJECTIVE

The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts.

METHODS

In this paper, we defined depression factors, which include negative events, negative emotions, symptoms, and negative thoughts from web posts. We proposed an enhanced event extraction (E3) method to automatically extract negative event terms. In addition, we also proposed an event-driven depression tendency warning (EDDTW) model to predict the depression tendency of web bloggers or post authors by analyzing their posted articles.

RESULTS

We compare the performance among the proposed EDDTW model, negative emotion evaluation (NEE) model, and the diagnostic and statistical manual of mental disorders-based depression tendency evaluation method. The EDDTW model obtains the best recall rate and F-measure at 0.668 and 0.624, respectively, while the diagnostic and statistical manual of mental disorders-based method achieves the best precision rate of 0.666. The main reason is that our enhanced event extraction method can increase recall rate by enlarging the negative event lexicon at the expense of precision. Our EDDTW model can also be used to track the change or trend of depression tendency for each post author. The depression tendency trend can help doctors to diagnose and even track depression of web post authors more efficiently.

CONCLUSIONS

This paper presents an E3 method to automatically extract negative event terms in web posts. We also proposed a new EDDTW model to predict the depression tendency of web posts and possibly help bloggers or post authors to early detect major depressive disorder.