Method: Generalized logistic growth modelling (GLM) approach was adopted to make prediction of growth of cases according to each state in Malaysia. The data was obtained from official Ministry of Health Malaysia daily report, starting from 26 September 2020 until 1 January 2021.
Result: Sabah, Johor, Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021. Nationally, the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order (MCO). The growth factor range for Sabah ranged from 1.00 to 1.25, while Selangor, the state which has the highest case, has a mean growth factor ranging from 1.22 to 1.52. The highest growth rates reported were in WP Labuan for the time periods of 22 Nov - 5 Dec 2020 with growth rates of 4.77. States with higher population densities were predicted to have higher cases of COVID-19.
Conclusion: GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time. This forecast could assist government in devising short- and long-term plan to tackle the ongoing pandemic.
Methods: 260 candidates were grouped into two separate geographical groups - urban and suburban/rural. Descriptive analysis, skewness and kurtosis were performed for normality assessment, whereas Cronbach's alpha, McDonald's omega, and Greatest lower bound assessed internal consistency. For validity measures, correlations were calculated between scores for separate stations, overall scores, urban and suburban/rural status. Also, exploratory factor analysis was performed on the five stations as validity measures. Difficulty and discrimination indices were calculated as quality measures. Qualitative analysis was performed on "red flag" comments detailing grossly unsuitable candidates.
Results: Roleplayer-driven stations yielded more red flags than examiner-driven stations. The three examiner-driven stations were significantly and moderately correlated (rho between 0.602 and 0.609, p 0.530), whereas the stations were distributed equally in difficulty index.
Conclusion: The UMS MMI has identified specific skillsets that may be in short supply in our incoming medical students. Also, it illustrates the yawning gap between academic knowledge and 'translational' scientific knowledge and communication skills.
METHODS: A shared decision-making scale was developed using a qualitative research derived model and refined using Rasch and factor analysis. The scale was used by staff in the hospital for four consecutive years (n = 152, 121, 119 and 121) and by two independent patients' and carers' samples (n = 223 and 236).
RESULTS: Respondents had difficulty determining what constituted a decision and the scale was redeveloped after first use in patients and carers. The initial focus on shared decision-making was changed to shared problem-solving. Two factors were found in the first staff sample: shared problem-solving and shared decision-making. The structure was confirmed on the second patients' and carers' sample and an independent staff sample consisting of the first data-points for the last three years. The shared problem-solving and decision-making scale (SPSDM) demonstrated evidence of convergent and divergent validity, internal consistency, measurement invariance on longitudinal data and sensitivity to change.
CONCLUSIONS: Shared problem-solving was easier to measure than shared decision-making in this context.
PRACTICE IMPLICATIONS: Shared problem-solving is an important component of collaboration, as well as shared decision-making.