METHODS: Three questionnaires were developed to get the responses from healthcare administrators, workers, and clients, representing the three components of Questionnaires to Assess Workplace Violence Risk Factors (QAWRF). The domains of the questionnaires were developed based on The Chappell and Di Martino's Interactive Model of Workplace Violence, and the items were generated from 28 studies identified from a systematic review of the literature. Six experts, 36 raters, and 90 respondents were recruited to assess the content validity, face validity, and usability and reliability of the QAWRF respectively. Item and Scale Level Content Validity Index, Item and Scale Level Face Validity Index, and Cronbach's alpha values were determined for QAWRF-administrator, QAWRF-worker, and QAWRF-client.
RESULTS: The psychometric indices for QAWRF are satisfactory.
CONCLUSION: QAWRF holds good content validity, face validity, and reliability, and findings from QAWRF can contribute towards worksite-specific interventions that are expected to be resource efficient and more effective than general WPV interventions.
METHODS: QAWRF, a three-component instrument consisting of QAWRF-Administrators, QAWRF-Workers, and QAWRF-Clients, had previously undergone content validation, face validation, and internal consistency reliability testing. 965 respondents were recruited to examine the construct validity of QAWRF, and a subset of these (n = 90) were retested again at an interval of three weeks to assess its test-retest reliability. Confirmatory factor analysis (CFA) was performed, and fitness indices, average variance extracted, correlation coefficient, composite reliability, and intraclass correlation coefficient were determined.
RESULTS: QAWRF-Administrator, QAWRF-Worker, and QAWRF-Client had acceptable factor loadings (≥0.6), absolute fit (Root Mean Square Error of Approximation > 0.1), incremental fit (Confirmatory Fit Index and Tucker Lewis Index > 0.9), parsimonious fit (Chi-square/degree of freedom < 5), correlation coefficient between construct (≤0.85), discriminant validity index, and construct reliability (≥0.6). CFA supported a four-factor model for QAWRF-Administrator and QAWRF-Worker, and a two-factor model for QAWRF-Client.
CONCLUSION: QAWRF holds good construct validity and test-retest reliability. By using QAWRF, healthcare managers can identify specific WPV risk factors that are perceived by stakeholders as prevalent at a particular workplace, and these findings can contribute towards data-driven, worksite-specific, and targeted WPV interventions in healthcare settings that are expected to be resource-efficient and more effective than general WPV interventions.
METHODS: Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.
PRINCIPAL FINDINGS: Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.
CONCLUSIONS: The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.