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: The development of the MUAPHQ C-19 was conducted in two stages. Stage I resulted in the generation of the instrument's items (development), and stage II resulted in the performance of the instrument's items (judgement and quantification). Six-panel experts related to the study field and ten general public participated to evaluate the validity of the MUAPHQ C-19. The content validity index (CVI), content validity ratio (CVR) and face validity index (FVI) were analysed using Microsoft Excel.
RESULTS: There were 54 items and four domains, namely the understanding, attitude, practice and health literacy towards COVID-19, identified in the MUAPHQ C-19 (Version 1.0). The scale-level CVI (S-CVI/Ave) for every domain was above 0.9, which is considered acceptable. The CVR for all items was above 0.7, except for one item in the health literacy domain. Ten items were revised to improve the item's clarity, and two items were deleted due to the low CVR value and redundancy, respectively. The I-FVI exceeded the cut-off value of 0.83 except for five items from the attitude domain and four from the practice domains. Thus, seven of these items were revised to increase the clarity of items, while another two were deleted due to low I-FVI scores. Otherwise, the S-FVI/Ave for every domain exceeded the cut-off point of 0.9, which is considered acceptable. Thus, 50-item MUAPHQ C-19 (Version 3.0) was generated following the content and face validity analysis.
CONCLUSIONS: The questionnaire development, content validity, and face validity process are lengthy and iterative. The assessment of the instruments' items by the content experts and the respondents is essential to guarantee the instrument's validity. Our content and face validity study has finalised the MUAPHQ C-19 version that is ready for the next phase of questionnaire validation, using Exploratory and Confirmatory Factor Analysis.
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.