13 research outputs found
KAP-C Questionnaire/Survey Tool - Psychometric Properties and Assessment of Knowledge, Attitude, and Practice Towards ChatGPT in Pharmacy Practice and Education
No full textThe KAP-C questionnaire or survey tool was developed and validated to assess the knowledge, attitude, and practice of pharmacists and pharmacy students towards ChatGPT in pharmacy practice and education. ChatGPT is an advanced conversational artificial intelligence (AI). This KAP-C tool aims to uncover insights into how ChatGPT's capabilities might be resourceful for pharmacy practice and education, particularly in low- and middle-income countries (LMICs) like Nigeria, Pakistan, and Yemen. The KAP-C tool development and validation processes involved a comprehensive literature search to identify relevant constructs, content validation by a panel of experts for items' relevancy using the content validity index (CVI), face validation by sample participants for items clarity using the face validity index (FVI), readability and difficulty index using the Flesch-Kincaid Readability Test, Gunning Fog Index, or Simple Measure of Gobbledygook (SMOG), assessment of reliability using internal consistency (Cronbach’s alpha), and exploratory factor analysis (EFA) to determine the underlying factor structures (eigenvalues, scree plot analysis, factor loadings, and varimax). The protocol describing all the procedures involved in the development and validation of the KAP-C tool has been published elsewhere and is here (https://doi.org/10.1007/s40615-023-01696-1) or on request from the author.</p
Risk factors associated with mortality among patients with novel coronavirus disease (COVID-19) in Africa
No full textBackground
The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in China and later spread rapidly to other parts of the world, including Africa. Africa was projected to be devastated by COVID-19. There is currently limited data regarding regional predictors of mortality among patients with COVID-19. This study aimed to evaluate the independent risk factors associated with mortality among patients with COVID-19 in Africa.
Methods
A total of 1028 confirmed cases of COVID-19 from Africa with definite survival outcomes were identified retrospectively from an open-access individual-level worldwide COVID-19 database. The live version of the dataset is available at https://github.com/beoutbreakprepared/nCoV2019. Multivariable logistic regression was conducted to determine the risk factors that independently predict mortality among patients with COVID-19 in Africa.
Results
Of the 1028 cases included in study, 432 (42.0%) were females with a median (interquartile range, IQR) age of 50 (24) years. Older age (adjusted odds ratio {aOR} 1.06; [95% confidence intervals {95% CI}, 1.04–1.08]), presence of chronic disease (aOR 9.63; [95% CI, 3.84–24.15]), travel history (aOR 2.44; [95% CI, 1.26–4.72]), as well as locations of Central Africa (aOR 0.14; [95% CI, 0.03–0.72]) and West Africa (aOR 0.12; [95% CI, 0.04–0.32]) were identified as the independent risk factors significantly associated with increased mortality among the patients with COVID-19.
Conclusions
The COVID-19 pandemic is evolving gradually in Africa. Among patients with COVID-19 in Africa, older age, presence of chronic disease, travel history, and the locations of Central Africa and West Africa were associated with increased mortality. A regional response should prioritize strategies that will protect these populations. Also, conducting a further in-depth study could provide more insights into additional factors predictive of mortality in COVID-19 patients
Psychometric properties and assessment of knowledge, attitude, and practice towards ChatGPT in pharmacy practice and education: a study protocol
No full textChatGPT represents an advanced conversational artificial intelligence (AI), providing a powerful tool for generating human-like responses that could change pharmacy prospects. This protocol aims to describe the development, validation, and utilization of a tool to assess the knowledge, attitude, and practice towards ChatGPT (KAP-C) in pharmacy practice and education. The development and validation process of the KAP-C tool will include a comprehensive literature search to identify relevant constructs, content validation by a panel of experts for items relevancy using content validity index (CVI) and face validation by sample participants for items clarity using face validity index (FVI), readability and difficulty index using the Flesch-Kincaid Readability Test, Gunning Fog Index, or Simple Measure of Gobbledygook (SMOG), assessment of reliability using internal consistency (Cronbach’s alpha), and exploratory factor analysis (EFA) to determine the underlying factor structures (eigenvalues, scree plot analysis, factor loadings, and varimax). The second phase will utilize the validated KAP-C tool to conduct KAP surveys among pharmacists and pharmacy students in selected low- and middle-income countries (LMICs) (Nigeria, Pakistan, and Yemen). The final data will be analyzed descriptively using frequencies, percentages, mean (standard deviation) or median (interquartile range), and inferential statistics like Chi-square or regression analyses using IBM SPSS version 28. A p<0.05 will be considered statistically significant. ChatGPT holds the potential to revolutionize pharmacy practice and education. This study will highlight the psychometric properties of the KAP-C tool that assesses the knowledge, attitude, and practice towards ChatGPT in pharmacy practice and education. The findings will contribute to the potential ethical integration of ChatGPT into pharmacy practice and education in LMICs, serve as a reference to other economies, and provide valuable evidence for leveraging AI advancements in pharmacy
Prognostic models of mortality following first‐ever acute ischemic stroke: A population‐based retrospective cohort study
No full textBackground and Aims: There is a lack of population‐based studies focusing on guideline‐based prognostic models for stroke. This study aimed to develop and validate a prognostic model that predicts mortality following a first‐ever acute ischemic stroke. Methods: The study included 899 adult patients ( ≥ 18 years) with confirmed diagnosis of first‐ever acute ischemic stroke enrolled in the Malaysian National Stroke Registry (NSR) from January 2009 to December 2019. The primary outcome was mortality within 90 days post‐stroke (266 events [29.6%]). The prognostic model was developed using logistic regression (75%, n = 674) and internally validated (25%, n = 225). Model performance was assessed using discrimination (area under the curve (AUC]) and calibration (Hosmer‐Lemeshow test [HL]). Results: The final model includes factors associated with increased risk of mortality, such as age (adjusted odds ratio, aOR 1.06 [95% confidence interval, CI 1.03, 1.10; p < 0.001]), National Institutes of Health Stroke Scale (NIHSS) score aOR 1.08 (95% CI 1.08, 1.13; p = 0.004), and diabetes aOR 2.29 (95% CI 1.20, 4.37; p = 0.012). The protective factors were antiplatelet within 48 h. aOR 0.40 (95% CI 0.19, 0.81; p = 0.01), dysphagia screening aOR 0.30 (95% CI 0.15, 0.61; p = 0.001), antiplatelets upon discharge aOR 0.17 (95% CI 0.08, 0.35; p < 0.001), lipid‐lowering therapy aOR 0.37 (95% CI 0.17, 0.82; p = 0.01), stroke education aOR 0.02 (95% CI 0.01, 0.05; p < 0.001) and rehabilitation aOR 0.08 (95% CI 0.04, 0.16; p < 0.001). The model demonstrated excellent performance (discrimination [AUC = 0.94] and calibration [HL, X2 p = 0.63]). Conclusion: The study developed a validated prognostic model that excellently predicts mortality after a first‐ever acute ischemic stroke with potential clinical utility in acute stroke care decision‐making. The predictors could be valuable for creating risk calculators and aiding healthcare providers and patients in making well‐informed clinical decisions during the stroke care process
Factors associated with acceptance of COVID-19 vaccine among University health sciences students in Northwest Nigeria
No full textStudents of the health sciences are the future frontliners to fight pandemics. The students’ participation in COVID-19 response varies across countries and are mostly for educational purposes. Understanding the determinants of COVID-19 vaccine acceptability is necessary for a successful vaccination program. This study aimed to investigate the factors associated with COVID-19 vaccine acceptance among health sciences students in Northwest Nigeria. The study was an online self-administered cross-sectional study involving a survey among students of health sciences in some selected universities in Northwest Nigeria. The survey collected pertinent data from the students, including socio-demographic characteristics, risk perception for COVID-19, and willingness to accept the COVID-19 vaccine. Multiple logistic regression was used to determine the predictors of COVID-19 vaccine acceptance. A total of 440 responses with a median (interquartile range) age of 23 (4.0) years were included in the study. The prevalence of COVID-19 vaccine acceptance was 40.0%. Factors that independently predict acceptance of the vaccine were age of 25 years and above (adjusted odds ratio, aOR, 2.72; 95% confidence interval, CI, 1.44–5.16; p = 0.002), instructions from heads of institutions (aOR, 11.71; 95% CI, 5.91–23.20; p<0.001), trust in the government (aOR, 20.52; 95% CI, 8.18–51.51; p<0.001) and willingness to pay for the vaccine (aOR, 7.92; 95% CI, 2.63–23.85; p<0.001). The prevalence of COVID-19 vaccine acceptance among students of health sciences was low. Older age, mandate by heads of the institution, trust in the government and readiness to pay for the vaccine were associated with acceptance of the vaccine. Therefore, stakeholders should prioritize strategies that would maximize the vaccination uptake
Flowchart of recruitment process of the study respondents.
No full textFlowchart of recruitment process of the study respondents.</p
COVID-19 vaccine acceptability among health sciences students in Northwest Nigeria
No full textAnonymized survey dataset of COVID-19 vaccine acceptability among health sciences students in Northwest Nigeri
COVID-19 risk perception and acceptance of the vaccine.
No full textCOVID-19 risk perception and acceptance of the vaccine.</p
