COVID-19 vaccine acceptance and associated factors among pregnant and lactating women attending maternity care clinics in refugee camps in Jordan.

BACKGROUND: Despite the advantages of vaccination in preventing maternal and fetal problems, there were many concerns in the medical community regarding vaccine safety for pregnant women, and this has put obstetricians in a challenging situation when it comes to advising their pregnant patients on whether to obtain the vaccine. AIM: This study was performed to define the level of acceptance of COVID-19 vaccination and assess the impact of COVID-19 attitudes and knowledge on vaccine acceptance between pregnant and lactating Syrian women who are seeking prenatal care services at the clinics in Azraq refugee camp in Jordan. METHOD: A quantitative, cross-sectional study utilizing a non-probability convenience sample. A validated and reliable self-administered questionnaire consisting of four sections was used. RESULTS: A total of 412 pregnant/lactating women was recruited The acceptance rate of the COVID-19 vaccine among participants was 86.5%. There was a significant positive moderate association between respondents' attitudes and knowledge around the COVID-19 vaccine and their acceptance of the vaccine (r = .468, p < .001, r = .357, p < .001), respectively. CONCLUSION: To effectively mitigate the COVID-19 pandemic and achieve collective protection, decision-makers must intensify the efforts in promoting the importance of maternal vaccination, especially in vulnerable communities that suffer the most from pandemic outcomes.

Jordanian nursing students' knowledge and attitudes of corneal donation.

A high success rate of corneal transplants is evident. However, there is still a lack of corneal grafts available to meet demand, largely because donors are reluctant to donate. Given their critical role in future healthcare teaching and advocacy. There has not been much research on Jordanian nursing students' perspectives on corneal donation, so it's critical to identify and eliminate any obstacles. This study aims to evaluate the knowledge and attitudes of Jordanian nursing students concerning corneal donation. A cross-sectional, descriptive design was used to recruit (n = 440) nursing students from four Jordanian universities. A self-reported questionnaire was used to obtain data on knowledge and attitudes regarding corneal donation. The average age of senior nursing students was (M = 23.07, SD = 3.63) years. Varying levels of understanding were revealed amongst university students toward corneal donation items. Generally, good attitude of nursing students toward corneal donation (M = 34.1, SD = 8.1). Weak positive relationship was found between total knowledge scores and age (r = 0.141, p = 0.003) while there is no significant relationship between age and total attitude score (r = 0.031, p = 0.552). Age was found to be a significant predictor (B = 0.01, Beta = 0.12, t = 2.07, p = 0.04). Also, the educational level of fathers is a significant positive predictor (Beta = 0.128, p = 0.008) for the total attitude scores among nursing students. Limited awareness of corneal donation, highlighting the need for focused educational interventions to improve their comprehension.

Effectiveness of blended learning basic life support module on knowledge and skills: A systematic review of randomized controlled trials.

Aim: To examine the effectiveness of the BLS blended learning module on knowledge and skills of BLS compared to the traditional module. Method: Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were utilized using key words to searched PubMed, Web of Science, and Cochrane Library for the studies published between January 2018 to May 2022. The risk of bias was assessed utilizing the Joanna Briggs Institute (JBI) critical appraisal checklist. Two reviewers separately extracted data from the included trials using a standardized data extraction form. Results: From 400 articles retrieved by the initial search, 11 studies were found to be eligible. Most studies' participants were laypersons (80.9 %), and the rest were either nursing (12.6 %) or medical students (6.5 %). The review shows superiority of utilizing the blended strategy in applying the BLS module in skills and knowledge retention, rather than using the traditional learning, which could improve the quality and outcomes of patients. Conclusions: Blended learning is effective in teaching BLS like the traditional face-to-face method, but more advantages of the blended learning module include improvement in retaining knowledge, skills acquisition, patient outcomes, and cost saving. The COVID-19 pandemic made blended learning crucial and using this method in BLS was effective and efficient. Future research to assess the effectiveness of blended learning on patient outcomes is recommended.

Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study.

BACKGROUND: Many current studies have claimed that the actual risk of heart disease among women is equal to that in men. Using a large machine learning algorithm (MLA) data set to predict mortality in women, data mining techniques have been used to identify significant aspects of variables that help in identifying the primary causes of mortality within this target category of the population. OBJECTIVE: This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique-based MLA. METHODS: A retrospective design was used to retrieve big data from the electronic health records of 2028 women with heart disease. Data were collected for Jordanian women who were admitted to public health hospitals from 2015 to the end of 2021. We checked the extracted data for noise, consistency issues, and missing values. After categorizing, organizing, and cleaning the extracted data, the redundant data were eliminated. RESULTS: Out of 9 artificial intelligence models, the Chi-squared Automatic Interaction Detection model had the highest accuracy (93.25%) and area under the curve (0.825) among the build models. The participants were 62.6 (SD 15.4) years old on average. Angina pectoris was the most frequent diagnosis in the women's extracted files (n=1,264,000, 62.3%), followed by congestive heart failure (n=764,000, 37.7%). Age, systolic blood pressure readings with a cutoff value of >187 mm Hg, medical diagnosis (women diagnosed with congestive heart failure were at a higher risk of death [n=31, 16.58%]), pulse pressure with a cutoff value of 98 mm Hg, and oxygen saturation (measured using pulse oximetry) with a cutoff value of 93% were the main predictors for death among women. CONCLUSIONS: To predict the outcomes in this study, we used big data that were extracted from the clinical variables from the electronic health records. The Chi-squared Automatic Interaction Detection model-an MLA-confirmed the precise identification of the key predictors of cardiovascular mortality among women and can be used as a practical tool for clinical prediction.

Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.

PURPOSE: This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease. METHODS: This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore. RESULTS: Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning. CONCLUSION: Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.