Development of stigma scale for women with mental illness in perinatal period, validity and reliability study.

AIM: Although there are many scales that measure stigma, there is no scale with the necessary adequacy to measure stigma in the perinatal period. The study aims to develop the stigma scale for women with mental illness in the perinatal period and test its validity and reliability. MATERIALS AND METHODS: Participants were reached via patients, visitors, and hospital staff who applied to Sakarya Training and Research Hospital between 01/06/2022 and 01/12/2022. Two hundred people (female n = 134, male n = 66) aged 18-65 participated in the study and "Sociodemographic data form," "Perinatal Mental Illness Stigma Scale (PMISS)," "Social Distance Scale," and "Beliefs Towards Mental Illness Scale" were used to collect data. Data were analyzed using SPSS 22 and the AMOS 26 program. RESULTS: The Content Validity Index of the scale items was between 0.80-1. Cronbach's alpha coefficient score of the general scale was 0.94, the "Discrimination and Prejudice" sub-dimension was 0.93, and the "Labeling" sub-dimension was 0.88. It was determined that item-total score correlations varied between 0.410 and 0.799. P value calculated < 0.05 in Barlett's test and 0.94 in the Kaiser-Meyer Olkin test. These values show that factor analysis can be applied to the scale. According to the Exploratory Factor Analysis result, the scale has a 2-factor structure, explaining 60% of the total variance. The Guttman Split-Half coefficient of the scale was 0.882, and the Spearman-Brown coefficient was 0.883. The scale was reapplied to 30 participants with an interval of three weeks. The correlation coefficient between the two measurements was 0.91, indicating that the scale satisfies the invariance principle over time. CONCLUSION: The PMISS is a reliable measurement tool that can be used to investigate stigma towards mental illness during the perinatal period in the Turkish population.

Development of a mobile monitoring program for anxiety and depression in pregnancy and evaluation of 3-month results.

BACKGROUND: The aims of this study were to develop a mobile mental health application (app) to scan the symptoms of anxiety, depression, and related factors during pregnancy; examine the effect of the app on pregnant women; and determine the factors related to using such an app. METHODS: A software platform called Perinatal Anxiety Depression Monitoring Platform (PADIP) was developed. This study included 320 pregnant women: 160 in the PADIP group and 160 in the control group. The PADIP group was screened monthly for 3 months for depression, anxiety, and sleep quality, and instant feedback was provided on scale scores. RESULTS: During the follow-up period, there was a significant decrease in depression and anxiety scale scores in the PADIP group but no significant difference in scale scores in the control group. The interface used for the app was important for scale scores. It was preferred by pregnant women with a high education level, higher Perinatal Anxiety Screening Scale scores, and lower sleep quality scores. CONCLUSIONS: PADIP use was associated with a decrease in depression and anxiety scores of pregnant women. It was more useful for patients with higher education levels and a history of a psychiatric disorder, but further research is needed to develop a more comprehensive model.

Detection of depression and anxiety in the perinatal period using Marine Predators Algorithm and kNN.

Undiagnosed prenatal anxiety and depression have the potential to worsen and have an adverse effect on both the mother and the infant. Although the diagnosis is made by specialist doctors, it is unclear which parameters are more effective. Especially in medicine, it is crucial to diagnose disease with high accuracy. For this reason, in this study, a questionnaire study was first conducted on pregnant women, and real original data were collected. Then, the Marine Predators Algorithm (MPA), one of the current metaheuristic algorithms inspired by nature, was combined with K-Nearest Neighbors (kNN) to determine high-priority features in the collected data. As a result, five of the 147 features selected by the proposed method were determined as high priority and approved by the doctors. In addition, the proposed method is compared with the Chi-square method, which is one of the filter-based feature selection methods. Thanks to the proposed feature selection method based on MPA and kNN, it has been observed that the classification gives more successful results in a shorter time with 98.11% success, and the model supports the diagnosis stage of the doctors.