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The quality and safety education for nurses (QSEN) competency program represents a valuable initiative in nursing practice and education, equipping nurses with the essential knowledge, attitude, and skills (KAS) required to deliver safe, efficient, and patient-centered care. This study aims to determine the impact of QSEN competency on the KAS of nurses in Palestine. A quasi-experimental pre-test and post-test design with two groups was used utilizing a questionnaire to collect data from 164 Junior nurses in two governmental hospitals within the period of 25th, January to the 10th February 2024. Patricia Benner's theory suggests that a strong educational foundation and diverse experiences enable nurses to enhance their patient care knowledge and abilities over time. The findings indicate that nurses in Palestine can benefit from targeted interventions and QSEN educational programs aimed at improving their patient-centered care competence, as post-test scores show a significant rise over pre-test scores. Junior nurses who participated in the QSEN program experienced a 57% increase in knowledge, a 57% increase in skills, and a 64% increase in attitudes. The intervention significantly improved knowledge (77.02 vs. 49.19, p < 0.001), quality and safety skills (70.16 vs. 44.61, p < 0.001), and attitudes (75.47 vs. 46.16, p < 0.001) among participants post-procedure, indicating a substantial positive impact on these areas, demonstrating the effectiveness of the educational intervention. The study demonstrates that an educational intervention improves junior nurses' KSAs for six QSEN competencies, leading to higher average scores in quality and safety competence subscales, thereby enhancing staff satisfaction, and reducing medical errors, and patient safety.

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. This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique-based MLA. 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. 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. 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.

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. 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. A quantitative, cross-sectional study utilizing a non-probability convenience sample. A validated and reliable self-administered questionnaire consisting of four sections was used. 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. 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.

Background and aim: Worldwide, coronary heart disease (CHD) is the main cause of death. To prevent heart disease and save lives, this study uses a machine learning algorithm (MLA), a subfield of artificial intelligence, to predict death vs. life outcomes among older persons with CHD. Methods: Large-scale data was retrieved from the electronic health records of 3,331 elderly patients with congestive heart failure retrospectively. Information was gathered on the population in Jordan who were hospitalized in public health hospitals between 2015 and 2021. Results: Based on the accuracy level (91.4%) and area under the curve (71.7%) of the eight prediction models created, the Chi-square automatic interaction detector algorithm was chosen to predict death versus life among older adults with CHD. The sequence of death prediction algorithms began with the medical diagnosis, location, age, and pulse pressure. Conclusion: Attempts should be made to use the expertise of many specialists and clinical screening data gathered from patient databases to speed up the diagnosis process with MLAs, which are thought to be a useful tool for identifying CHD patients who are at high risk of dying.

Background: Cardiovascular health and preventative strategies are influenced by the sex of the individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, or invasive treatments to slow disease progression and improve outcomes. Aim: To compare and predict the differences in the primary sources of IHD burden between males and females in various age groups, geographical regions, death versus alive, and comorbidity levels. Methods: A predictive and retrospective design was implemented in this study. Electronic health records were extracted, which were equally distributed among males and females with IHD. The dataset consisted of patients who were admitted between 2015 and 2022. Two of the eight models generated by Modeler software were implemented in this study: the Bayesian network model, which achieved the highest area under curve score (0.600), and the Chi-squared automatic interaction detection (CHAID) model, which achieved the highest overall accuracy score (57.199%). Results: The study sample included 17,878 men and women, 58% of whom had no comorbidities and 1.7% who died. Age, the Charlson comorbidity index score, and geographical location all predicted IHD, but age was more influential. Bayesian network analysis showed that IHD odds were highest in males 40-59 and females 60-79, with the highest mortality risk in females 80-100. North and south Jordan had higher IHD rates and middle-aged males from north and middle governorates had higher IHD rates according to CHAID. Conclusion: By using artificial intelligence, clinicians can improve patient outcomes, treatment quality, and save lives in the fight against cardiovascular illnesses. To predict IHD early, machine-learning algorithms can analyze complex patient data patterns to improve outcomes.

Introduction Given the vital nature of their profession, ICU nurses endure significant psychological and physical stress. Burnout, low job satisfaction, and deteriorated patient care might result from the high-stress atmosphere. Objectives This study aims to assess the level of alarm fatigue and stress among nurses who work in acute care units. Methods A descriptive design was used to recruit nurses in acute care units. A self-administered questionnaire was used to collect the required data composed of three parts, namely demographical data; the alarm fatigue part, which was created by Torabizadeh et al. and composed of 13 items, and the perceived stress scale (PSS) which is a psychological diagnostic instrument created to assess how much people find their daily lives to be stressful. It was created by Cohen et al. and composed of 10 items. Results An average age of (35.3 ± 6.24) years, and an average number of years of experience of (7.63 ± 5.56), were found among the 128 nurses that were recruited. Acute care nurses had a significant degree of alarm fatigue, as indicated by the overall alarm fatigue score of (M = 30.1 ± SD = 7.47). A moderate degree of stress was also indicated by the overall perceived stress score, which was (M = 21.5 ± 5.02). Among nurses, alarm fatigue and felt stress are not correlated with any demographic feature, including sex, educational attainment, marital status, and working location. Conclusion Stress and alarm fatigue are serious problems for acute care units that can jeopardize nurse and patient safety. The implementation of methods that mitigate alarm fatigue and stress, such as alarm customization, adequate staffing, and support systems, can enhance the work environment in acute care units. Healthcare companies can raise the grade of care provided to patients and enhance the general well-being and job satisfaction of their nursing staff by addressing these challenges.

Background Fear of childbirth (FOC) is a prevalent psychological issue among pregnant women globally, necessitating valid and culturally adapted assessment tools. Purpose The study aimed to examine the reliability, validity, and factor structure of the Arabic version of the Childbirth Fear Questionnaire (CFQ) to ensure its suitability for use in the Jordanian context. Methods A cross-sectional study was conducted with 452 pregnant women attending prenatal clinics in northern Jordan to assess the psychometric properties of a questionnaire. The evaluation looked at how well the questionnaire was designed and understood, then used exploratory factor analysis (EFA) with IBM SPSS 29.0 and confirmatory factor analysis (CFA) with IBM SPSS Amos 26.0 to check its structure. Results EFA identified four distinct factors: fear of harm to the baby (α = 0.88), fear of pain (α = 0.82), fear of body damage from vaginal birth (α = 0.80), and fear of loss of sexual pleasure/attractiveness (α = 0.85). The overall Cronbach's alpha was 0.92, indicating excellent reliability. CFA supported the four-factor model, though some fit indices suggested moderate model fit. Conclusion The Arabic version of the CFQ demonstrated robust psychometric properties, making it a reliable and valid tool for assessing childbirth fear among Jordanian women. Its use can aid in the early identification of FOC, guiding targeted interventions to improve maternal and neonatal outcomes.

AbstractIntroduction: The prevalence of obesity and overweight has risen to an epidemic level globally, posing significant challenges to healthcare systems. Studies revealed that individuals with obesity and overweight frequently face negative societal perceptions and are often blamed for their weight. Healthcare personnel are not exempt from biases associated with obesity, which can affect their interaction with patients. As frontline providers of care, healthcare professionals play a critical role in managing obesity and related health conditions. However, their attitudes toward individuals with obesity and overweight can influence the quality of care provided, patient satisfaction, and health outcomes. The current study assesses healthcare professionals’ attitudes about parturients living with overweight and obesity in northern Jordan, as well as the sociodemographic factors associated with their attitudes. Methods: Using a cross-sectional, descriptive design, this study recruited a convenience sample consisting of 62 obstetricians, 30 registered nurses, and 95 certified midwives from labor units. Participants completed a questionnaire concerning their sociodemographic characteristics, and Arabic versions of the Fat Phobia Scale (FPS) and Nurses' Attitudes toward Obesity and Obese Patients Scale (NATOOPS). Results: The overall mean scores of the sample on both scales indicated negative attitudes. Most of the sample was female, married, and aged 29 years. Midwives held more positive attitudes than did obstetricians and nurses. Most participants perceived parturients living with overweight and obesity as overate people, shapeless, slow, and unattractive. Younger participants with long years of experience held less negative attitudes than the rest of the sample. The ANOVA test results showed significant differences in attitudes toward parturients living with overweight and obesity based on age and educational level. Participants with PHD in medicine and a BS in midwifery held positive attitudes. Post hoc Tukey HSD test indicated that the mean (FPS) of the PhD holders and the bachelor's midwifery holders was significantly lower than that of the diploma in midwifery holders (p = 0.012 and p < 0.001, respectively). Conclusions: It is necessary to treat maternal obesity more adequately in both beginning education courses and continuing professional education seminars for working professionals.

Nurses with a high level of digital health literacy (DHL) play a key role in providing high-quality patient care and promoting self-care activities. This study assessed DHL among nurses in Jordanian hospitals. A cross-sectional, descriptive study design was used. Data were collected targeting 238 nurses conveniently from both public and private hospitals. A standard pre-designed tool was used to collect DHL data composed of 21 questions divided into seven subscales, each one having three items. These subscales are operational skills, navigation skills, information searching, evaluating reliability, determining relevance, adding self-generated health content, and protecting privacy. Participants mostly achieved very desirable results in operational skills, information searching, and navigational skills, with a percentage of total scores of 82.5%, 90.6%, and 81.7%, respectively. None of the demographics were significantly different from the total DHL score (p>0.05). This study provides essential insight into healthcare professionals’ DHL in Jordanian hospitals and their approach to seeking health information, determining relevancy and content, and maintaining privacy during the search for required information. Healthcare providers, including nurses, were at the frontlines in managing patients’ information effectively. These results indicate that a program to promote DHL level and skills in healthcare providers would be useful. Policymakers, health educators and public health practitioners engaged in health literacy programs might use the results of this study for informed decision-making, as well as to improve and enhance DHL levels.

Background: Assessing and quantifying anxiety levels among oncology professionals across different disciplines, along with evaluating the role of social support networks within healthcare institutions, can inform the development of targeted interventions aimed at enhancing staff engagement, translating research findings into practical workplace strategies, and ultimately reducing anxiety levels. Objective: This study aims to examine the perceptions among cancer-diagnosed workers across disciplines of workplace support systems in alleviating anxiety among employees with cancer in oncology settings in Jordan. Methods: A cross-sectional study was undertaken at the King Husain Cancer Center in Amman, Jordan. A proportionate sampling strategy was employed to select the sample population of 354 oncology professionals from various disciplines. Data were gathered using self-administered questionnaires on Generalized Anxiety Disorder-7, work-related issues, and work support systems. Results: The mean age of participants was 42.3 years. The majority of participants (n = 185, 52.3%) were of stage II cancer. In terms of treatment types received by the patients, the majority received chemotherapy (n = 325, 91.8%), while the remaining patients underwent surgery (n = 13, 3.7%). Pearson correlation was utilized to assess the relationship between anxiety disorders and variables of age and duration of diagnosis with cancer. The results demonstrated a statistically significant correlation with age (r = 0.49, p = 0.037) and duration of diagnosis (r = 0.61, p = 0.027). Conclusion: The study highlights the importance of workplace support systems in reducing anxiety among workers with cancer in Jordan, highlighting the need for structured and sustainable interventions to improve their well-being. This study highlights the importance of investing in workplace support programs for oncology workers with cancer, thereby raising job satisfaction, reducing burnout, and improving patient-care outcomes. Plain language summary Workplace support systems to reduce anxiety Background: A workplace support system is considered the most effective method for reducing anxiety among cancer patients, involving religious coping, optimism, denial, and family support. However, Jordanian literature suggests that patient safety culture is not fully implemented in hospitals. Design: A descriptive study cross-sectional correlational design was used. The study uses a cross-sectional design to gather data on nurses’ perceptions of support systems. Conclusion: The lack of high-quality research addressing the mental health of cancer survivors, the possible influence of long-term and late effects of cancer treatment, and the limited studies attentive to prevention. It emphasizes the importance of workplace support systems in reducing anxiety among oncology nurses.