This study was carried out to determine the knowledge regarding laboratory safety precautions amongst Allied Health Sciences students at the University of Sri Jayewardenepura. A cross-sectional study on laboratory safety knowledge of Allied Health Sciences students was conducted using a standardized, 60-item structured self-administered questionnaire. The questionnaires were administered to 229 of students. The statistical data was generated using SPSS 16th version. The students who obtained scores of ≥75, 74-60, 50-59 and ≤49 were categorized as "excellent", "good", "moderate" and "poor" knowledge, respectively 23.8% (n=20), poor 64.3 (n=54), B.Sc. in MLS: excellent 15.4% (n=16), good 49% (n=51), moderate 18.3% (n=19), poor 17.3% (n=18)
Porous polymer hydrogels with high mechanical properties and response time is such a unique coalition that may open up many new application avenues especially in biomaterials, nevertheless which is a challenging task to achieve. This work highlighted a novel yet simple approach on developing highly porous, fast swelling hydrogels having high mechanical performance by slightly twisting the traditional double network concept following emulsion template polymerization. The fabricated acrylamide hydrogel structure entails 40–70 micrometer size pores while having ultimate compressive strength of 5–7 MPa. Further, it exhibited approximately 400 wt% water absorptivity compared to the dry weight of the sample in 15 s at 30°C. Such enhanced swelling properties of the developed hydrogel are due to the surface roughness of interconnected capillary channels.
Background: Currently, people infected with HIV are largely discriminated and discredited. Misunderstanding about the mechanism of HIV transmission has been identified as one reason for discrimination. This study assessed the socio-demographic and behavioral risk factors of the two misconceptions about HIV transmission, namely HIV is transmitted by sharing cups and plates with an HIV infected person (Myth 1) and HIV is transmitted by mosquito bites (Myth 2) among the trainees who were selected to the Tertiary and Vocational Education Training (TVET) Centers in Sri Lanka.Methods: This study applied stratified random sampling to select 955 respondents. A self-administered structured questionnaire was used to collect the data. Generalized linear mixed model (GLMM) approach was applied to find the associations between misconceptions about HIV and socio-demographic and behavioral risk factors of the trainees.Results: Level of education of trainees, family relationship, knowledge on sexual and reproductive health (SRH), knowledge about the risk of getting HIV after sexual intercourse, whether the trainee had participated in seminar or workshop on sexually transmitted diseases were identified as the possible factors to detect the knowledge about the misconceptions of HIV transmission.Conclusions: Even though the level of education among different social segments have not revealed remarkable differences in knowledge, the study convinced that the youth should be provided better awareness and education on STD and HIV through countrywide workshops and awareness programmes.
Child Sexual Abuse has been a global epidemic with devastating consequences. One in four girls and one in six boys have been experienced some form of sexual abuse in their tender age in the world. According to Police statistics, Child Sexual Abuse (CSA) cases is growing in recent years in Sri Lanka too. Galle is among the four districts where the reported child abuse cases high and the reported CSA complaints are increasing extraordinarily. Also, there is no previous research have been done in the Southern part of the country regarding the crisis of CSA. So, main objective of this study is to determine the key risk factors that affected to a CSA in Galle Police Division, and to develop suitable regression and machine learning models to predict the severity of CSA. 225 CSA cases reported to Police Child and Women Bureau of Galle Police Division during the period 2017 – 2020 were treated for this study. Out of twenty-one risk which were found from literature and knowledge of domain experts, sixteen variables showed a significant relationship with response variable severity of CSA according to chi-square test of association. Traditional OLR model was performed to predict severity of CSA and to detect key risk factors to a CSA with two different data selection methods. Next, machine learning techniques: Decision Tree, SVM, and PNN were trained to classify severity of CSA. Random over-sampling technique was used to overcome the class imbalanced problem persists in the dataset. Finally, bagging technique was executed to conserve robustness of models and to improve performance. The OLR model classified the severity of CSA with 68.85% accuracy. Machine learning techniques, Decision Tree, SVM and PNN model classified the severity of CSA with an accuracy of 82.15%, 77.68% and 85.25% respectively. PNN model performed with higher accuracy better than other fitted models. The results obtained from this study can be used to take precautions and to arrange awareness sessions for adults to reduce CSA in Galle Police Division. Also, the study can be extended to the whole island to reduce CSA and to make it a better place for children.
In different fields of study, multivariate binary data is often found, especially when several different qualitative characteristics or attributes are measured in the same unit or from the same person. These bivariate or multivariate responses observed from the same individual or a unit are likely to be correlated. This study aimed to evaluate the influence on the regression estimates of the parameters when binary responses are modeled jointly. The correlation between binary outcomes was captured by incorporating random effects. Normal and bridge distributions were assumed for the random effects. A simulation study was performed to illustrate the impact on the marginal parameter estimates of the joint response model when using the bridge and normal distributions for the random effects. The simulation study revealed that the joint model with either normal or bridge random effects provides a better gain in efficiency in the parameter estimates compared to the individual models which assume responses are independent. Furthermore, the parameter estimates of the joint model are more or less the same under the normal distribution and bridge distribution of the random effects when outcomes are correlated. However, slight differences are noted in the standard errors of the parameter estimates. In addition, when two outcomes are not correlated there is no gain in the fitting joint model over separate univariate models. Finally, these methods were applied to the Bangladesh Demographic and Health Survey 2011 (BDHS 2011) data.
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