METHODS: This quasi-experimental study was conducted in the private medical institution in Malaysia. The same questionnaire was used to administer twice, before and after the posting. Moreover, a qualitative question on the issues related to family planning and contraception utilizations in Malaysia was added to the after posting survey. The quantitative data were analyzed using IBM SPSS (version 20) and qualitative data by RQDA software.
RESULTS: A total of 146 participants were recruited in this study. Knowledge on contraception method before posting was 5.11 (standard deviation [SD] ±1.36) and after posting was 6.35 (SD ± 1.38) (P < 0.001). Thematic analysis of the students' answer revealed four salient themes, which were as follows: (1) cultural barrier, (2) misconception, (3) inadequate knowledge, and (4) improvement for the health-care services.
CONCLUSIONS: The teaching-learning process at the MCH posting has an influence on their perception and upgraded their knowledge. It also reflects the role of primary health-care clinics on medical students' clinical exposure and training on family planning services during their postings.
AIMS: This study aimed to explore the postgraduate students' perspective on using Twitter as a learning resource.
SUBJECTS AND METHODS: This qualitative study was conducted as part of a postgraduate program at a university in the United Kingdom. A focus group discussion and five in-depth interviews were conducted after receiving the informed consent. The qualitative data were analyzed by R package for Qualitative Data Analysis software.
ANALYSIS USED: Deductive content analysis was used in this study.
RESULTS: Qualitative analysis revealed four salient themes, which were (1) background knowledge about Twitter, (2) factors influencing the usage of Twitter, (3) master's students' experiences on using Twitter for education, and (4) potential of using Twitter in the postgraduate study. The students preferred to use Twitter for sharing links and appreciated the benefit on immediate dissemination of information. Meanwhile, privacy concern, unfamiliarity, and hesitation to participate in discussion discouraged the students from using Twitter as a learning platform.
CONCLUSIONS: Using social media platforms in education could be challenging for both the learners and the educators. Our study revealed that Twitter was mainly used for social communication among postgraduate students however most could see a benefit of using Twitter for their learning if they received adequate guidance on how to use the platform. The multiple barriers to using Twitter were mainly related to unfamiliarity which should be addressed early in the learning process.
OBJECTIVE: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10.
METHODS: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview.
RESULTS: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88).
CONCLUSIONS: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.