METHODS: A total of 176 adolescents in selected urban areas in the states of Wilayah Persekutuan and Selangor were selected. The Suicide Ideation Scale (SIS) was used to measure the level of severity or tendency of suicidal ideation. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to measure the perceived social support received by the respondent while the Spiritual Wellbeing Scale (SWBS) was used to measure the religious wellbeing (RWB), the existential wellbeing (EWB) and the overall score of spiritual wellbeing (SWB).
RESULTS: The study found that both RWB and EWB showed significant negative correlation with suicidal ideation. Similarly, support from family and friends also showed a negative correlation with suicidal ideation. Further analysis using multiple regressions showed that RWB and SWB, and family support predict suicidal ideation in adolescents.
CONCLUSION: Spiritual wellbeing in combination with family support plays a major role in predicting suicidal ideation. Therefore, intervention for encompassing spirituality and family support may contribute to a more positive outcome in suicidal adolescents.
DESIGN AND METHODS: Participants were randomly selected using multi-stage sampling methods from three public universities in the study location. Participants completed measures of internet and smartphone addictions, depression, anxiety, stress, and suicidality, along with sociodemographic items.
FINDINGS: At bivariate level, both internet and smartphone addictions were found to have significant positive correlations with depression, anxiety, stress, and suicidality. At multivariate level, only internet addiction emerged as a consistent significant predictor for depression, anxiety, stress, and suicidality.
PRACTICE IMPLICATIONS: The present findings have implications for mental health professionals to routinely screen for psychological disturbance in young adults who have potential risks for internet addiction.
METHODS: Sixteen computed tomography scan of SC patients (8 months-6 years old) were imported to Materialise Interactive Medical Image Control System (MIMICS) and Materialise 3-matics software. Three-dimensional (3D) OC models were fabricated, and linear measurements were obtained. Mathematical formulas were used for calculation of OC volume and surface area from the 3D model. The same measurements were obtained from the software and used as ground truth. Data normality was investigated before statistical analyses were performed. Wilcoxon test was used to validate differences of OC volume and surface area between 3D model and software.
RESULTS: The mean values for OC surface area for 3D model and MIMICS software were 103.19 mm2 and 31.27 mm2, respectively, whereas the mean for OC volume for 3D model and MIMICS software were 184.37 mm2 and 147.07 mm2, respectively. Significant difference was found between OC volume (P = 0.0681) and surface area (P = 0.0002) between 3D model and software.
CONCLUSION: Optic canal in SC is not a perfect conical frustum thus making 3D model measurement and mathematical formula for surface area and volume estimation not ideal. Computer software remains the best modality to gauge dimensional parameter and is useful to elucidates the relationship of OC and eye function as well as aiding intervention in SC patients.