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All Journal JIKO(Jurnal Ilmiah Keperawatan Orthopedi) JIKO (Jurnal Ilmiah Keperawatan Orthopedi)
Hernida Dwi Lestari
Department of Nursing, Sismadi Health College, Indonesia
Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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The Relationship Between Self-Stigma Levels and Medication Compliance in Patients with Severe Mental Disorders Hernida Dwi Lestari; Desy Pramujiwati; Didi Sutisna; Ewin Suciana
JIKO (Jurnal Ilmiah Keperawatan Orthopedi) Vol. 9 No. 1 (2025): JIKO (Jurnal Ilmiah Keperawatan Orthopedi)
Publisher : Unit Penelitian dan Pengabdian Masyarakat STIKES Fatmawati Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46749/60esfc20

Abstract

Background: Medication adherence is a key factor in the successful treatment of patients with severe mental illness. However, the level of non-adherence remains relatively high and is often associated with various psychosocial factors, one of which is self-stigma. Self-stigma can influence patients' perceptions of the disease and treatment, potentially reducing adherence to regular medication use. Objective: This study aims to analyze the relationship between self-stigma levels and medication adherence in patients with severe mental illness. Methods: This study used a quantitative design with a cross-sectional approach. A total of 220 patients with severe mental illness undergoing treatment at a mental health service facility became the study respondents. The level of self-stigma was measured using the Internalized Stigma of Mental Illness (ISMI), while medication adherence was measured using the Medication Adherence Rating Scale (MARS). Data analysis was performed descriptively and inferentially using the Spearman correlation test and logistic regression with a significance level of p < 0.05. Results: The results showed that most respondents had moderate to high levels of self-stigma and more than half of respondents were non-compliant with medication. There was a significant negative relationship between self-stigma and medication adherence (r = −0.462; p < 0.001). Patients with high self-stigma are at greater risk of non-adherence to treatment. Conclusion: Self-stigma is significantly associated with medication adherence in patients with severe mental illness. Interventions focused on reducing self-stigma need to be integrated into mental health services to improve medication adherence and patient outcomes.
Application of Artificial Intelligence for Early Detection of Pregnancy Complications Ewin Suciana; Didi Sutisna; Hernida Dwi Lestari
J I K O (Jurnal Ilmiah Keperawatan Orthopedi) Vol 9, No 1 (2025): JIKO (Jurnal Ilmiah Keperawatan Orthopedi)
Publisher : LPPM AKPER FATMAWATI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46749/jiko.v9i1.217

Abstract

Pregnancy complications remain a major cause of high maternal and infant morbidity and mortality rates in various countries. Delays in detecting complications such as preeclampsia, gestational diabetes, preterm labor, and hypertensive disorders of pregnancy can lead to conditions that threaten the safety of both the mother and the fetus. The development of Artificial Intelligence (AI) technology provides opportunities to improve early detection capabilities through rapid and accurate clinical data analysis. This study aims to develop and evaluate an AI system for early detection of pregnancy complications using a Hybrid Research and Development (H&R) design. The research stages include needs analysis, data collection, model development, system validation, and prototype implementation. The data used comes from medical records of pregnant women, including age, blood pressure, body mass index, blood glucose levels, gestational age, and history of previous complications. Several machine learning algorithms were tested, namely Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, and Artificial Neural Network. The results showed that the Random Forest algorithm provided the best performance with an accuracy rate of 92%, sensitivity of 94%, and specificity of 90%. The most influential variables in prediction were blood pressure, history of preeclampsia, and body mass index. The system implementation demonstrated high user acceptance, as it helped healthcare workers identify high-risk pregnant women more quickly and accurately. Therefore, the application of AI has the potential to become an effective clinical decision support system for improving the quality of antenatal care and supporting efforts to reduce maternal and infant mortality.
The Relationship Between Self-Stigma Levels and Medication Compliance in Patients with Severe Mental Disorders Hernida Dwi Lestari; Desy Pramujiwati; Didi Sutisna; Ewin Suciana
J I K O (Jurnal Ilmiah Keperawatan Orthopedi) Vol 9, No 1 (2025): JIKO (Jurnal Ilmiah Keperawatan Orthopedi)
Publisher : LPPM AKPER FATMAWATI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46749/jiko.v9i1.205

Abstract

Background: Medication adherence is a key factor in the successful treatment of patients with severe mental illness. However, the level of non-adherence remains relatively high and is often associated with various psychosocial factors, one of which is self-stigma. Self-stigma can influence patients' perceptions of the disease and treatment, potentially reducing adherence to regular medication use. Objective: This study aims to analyze the relationship between self-stigma levels and medication adherence in patients with severe mental illness. Methods: This study used a quantitative design with a cross-sectional approach. A total of 220 patients with severe mental illness undergoing treatment at a mental health service facility became the study respondents. The level of self-stigma was measured using the Internalized Stigma of Mental Illness (ISMI), while medication adherence was measured using the Medication Adherence Rating Scale (MARS). Data analysis was performed descriptively and inferentially using the Spearman correlation test and logistic regression with a significance level of p < 0.05. Results: The results showed that most respondents had moderate to high levels of self-stigma and more than half of respondents were non-compliant with medication. There was a significant negative relationship between self-stigma and medication adherence (r = −0.462; p < 0.001). Patients with high self-stigma are at greater risk of non-adherence to treatment. Conclusion: Self-stigma is significantly associated with medication adherence in patients with severe mental illness. Interventions focused on reducing self-stigma need to be integrated into mental health services to improve medication adherence and patient outcomes
Co-Authors Desy Pramujiwati Didi Sutisna Ewin Suciana