METHODS: A cross-sectional study was conducted among N = 271 primary care physicians from 86 primary care practices throughout two states in Malaysia. Questionnaires used were specifically developed based on the TPB, consisting of both direct and indirect measures related to the provision of sickness leave. Questionnaire validity was established through factor analysis and the determination of internal consistency between theoretically related constructs. The temporal stability of the indirect measures was determined via the test-retest correlation analysis. Structural equation modelling was conducted to determine the strength of predictors related to intentions.
RESULTS: The mean scores for intention to provide patients with sickness was low. The Cronbach α value for the direct measures was good: overall physician intent to provide sick leave (0.77), physician attitude towards prescribing sick leave for patients (0.77) and physician attitude in trusting the intention of patients seeking sick leave (0.83). The temporal stability of the indirect measures of the questionnaire was satisfactory with significant correlation between constructs separated by an interval of two weeks (p
MATERIALS AND METHODS: A literature search was performed across PubMed, EMBASE, Emerald Insight and grey literature sources. The key terms used in the search include 'distribution', 'method', and 'physician', focusing on research articles published in English from 2002 to 2022 that described methods or tools to measure hospital-based physicians' distribution. Relevant articles were selected through a two-level screening process and critically appraised. The primary outcome is the measurement tools used to assess the distribution of hospital-based physicians. Study characteristics, tool advantages and limitations were also extracted. The extracted data were synthesised narratively.
RESULTS: Out of 7,199 identified articles, 13 met the inclusion criteria. Among the selected articles, 12 were from Asia and one from Africa. The review identified eight measurement tools: Gini coefficients and Lorenz curve, Robin Hood index, Theil index, concentration index, Workload Indicator of Staffing Need method, spatial autocorrelation analysis, mixed integer linear programming model and cohortcomponent model. These tools rely on fundamental data concerning population and physician numbers to generate outputs. Additionally, five studies employed a combination of these tools to gain a comprehensive understanding of physician distribution dynamics.
CONCLUSION: Measurement tools can be used to assess physician distribution according to population needs. Nevertheless, each tool has its own merits and limitations, underscoring the importance of employing a combination of tools. The choice of measuring tool should be tailored to the specific context and research objectives.
METHODS: Medical claims records from February 2019 to February 2020 were extracted from a health insurance claims database. Data cleaning and data analysis were performed using Python 3.7 with the Pandas, NumPy and Matplotlib libraries. The top five most common diagnoses were identified, and for each diagnosis, the most common medication classes and medications prescribed were quantified. Potentially inappropriate prescribing practices were identified by comparing the medications prescribed with relevant clinical guidelines.
KEY FINDINGS: The five most common diagnoses were upper respiratory tract infection (41.5%), diarrhoea (7.7%), musculoskeletal pain (7.6%), headache (6.7%) and gastritis (4.0%). Medications prescribed by general practitioners were largely as expected for symptomatic management of the respective conditions. One area of potentially inappropriate prescribing identified was inappropriate antibiotic choice. Same-class polypharmacy that may lead to an increased risk of adverse events were also identified, primarily involving multiple paracetamol-containing products, non-steroidal anti-inflammatory drugs (NSAIDs), and antihistamines. Other areas of non-adherence to guidelines identified included the potential overuse of oral corticosteroids and oral salbutamol, and inappropriate gastroprotection for patients receiving NSAIDs.
CONCLUSIONS: While prescribing practices are generally appropriate within the private primary care sector, there remain several areas where some potentially inappropriate prescribing occurs. The areas identified should be the focus in continuing efforts to improve prescribing practices to obtain the optimal clinical outcomes while reducing unnecessary risks and healthcare costs.
OBJECTIVE: To determine the validity and reliability of the PPCI for physicians in Malaysia.
SETTING: An urban tertiary hospital in Malaysia.
METHODS: This prospective study was conducted from June to August 2014. Doctors were grouped as either a "collaborator" or a "non-collaborator". Collaborators were doctors who regularly worked with one particular clinical pharmacist in their ward, while non-collaborators were doctors who interacted with any random pharmacist who answered the general pharmacy telephone line whenever they required assistance on medication-related enquiries, as they did not have a clinical pharmacist in their ward. Collaborators were firstly identified by the clinical pharmacist he/she worked with, then invited to participate in this study through email, as it was difficult to locate and approach them personally. Non-collaborators were sampled conveniently by approaching them in person as these doctors could be easily sampled from any wards without a clinical pharmacist. The PPCI for physicians was administered at baseline and 2 weeks later.
MAIN OUTCOME MEASURE: Validity (face validity, factor analysis and discriminative validity) and reliability (internal consistency and test-retest) of the PPCI for physicians.
RESULTS: A total of 116 doctors (18 collaborators and 98 non-collaborators) were recruited. Confirmatory factor analysis confirmed that the PPCI for physicians was a 3-factor model. The correlation of the mean domain scores ranged from 0.711 to 0.787. "Collaborators" had significantly higher scores compared to "non-collaborators" (81.4 ± 10.1 vs. 69.3 ± 12.1, p < 0.001). The Cronbach alpha for the overall PPCI for physicians was 0.949, while the Cronbach alpha values for the individual domains ranged from 0.877 to 0.926. Kappa values at test-retest ranged from 0.553 to 0.752.
CONCLUSION: The PPCI for physicians was a valid and reliable measure in determining doctors' views about collaborative working relationship with pharmacists, in Malaysia.