Background and aim: The Nurses' Cancer Pain Management Competency Scale is a tool to explore nurses' competencies and subjective experiences in cancer pain management, and to help nurses understand their current shortcomings in cancer pain management. Furthermore, based on the scale's specific score, nurses can evaluate their lack of understanding about cancer pain management, advance research into this area, and enhance their capacity to control cancer pain while providing patient care. The scale is currently available only in English and in Chinese. The aim of this study was to translate the new scale and measure its reliability and validity in the Italian context.
Study design: Methodological research model.
Methods: The population of this methodological study included Italian nurses working in the oncology departments of 21 hospitals in Northern, Southern and Central Italy; the sample involved 243 nurses who met the inclusion criteria.
Results: Cronbach's alpha of the scale was 0.814. The Guttman half-reliability of the scale was 0.819. Nurses' cancer pain management competency includes four factors, which accounted for 71.43% of the cumulative variance: the context of pain management, pain assessment and measurement, management of pain, and multidimensional nature of pain. On a 4-point scale for total competency, the mean score was 2.65 ±0.89. The multidimensional nature of pain (2.88 ±0.76) was the factor that showed the highest mean score, whereas the management of pain factor was the lowest (2.52 ±0.73).
Conclusion: Nurses' cancer pain management competency can be assessed using the Italian version of the Nurses' Cancer Pain Management Competency Scale, which has strong validity and reliability.
Introduction: Hand hygiene is the most cost-effective procedure for the prevention of healthcare-associated infections, but healthcare worker compliance is often insufficient.
Research design: The objective of this second cross-sectional study was to quantify hand hygiene compliance among the healthcare workers of a large teaching hospital, to explore associated factors and to compare results to those of the 2021 study.
Methods: In 2022, educational sessions were conducted within each hospital department during which hospital healthcare workers received tailored feedback on the hand hygiene compliance registered in the previous year. Then, one month later, direct observations of hand hygiene compliance with five World Health Organization recommendations were collected again by anonymous observers in each ward. Data were grouped by healthcare area (clinical, surgical and intensive care), and three multivariable logistic regression models were built to identify predictors of hand hygiene compliance.
Results: Overall, 5,426 observations were collected by 73 observers in three weeks. Hand hygiene compliance was 79.7%, 73.5% and 63.1% in clinical, surgical and intensive care areas, respectively, increasing in clinical wards but decreasing in surgical departments compared to the 2021 study. The multivariable analyses showed that hand hygiene compliance after patient contact was consistently higher than before patient contact, while there was some variability in compliance with other factors across the three areas.
Conclusion: The study found suboptimal adherence to good hand hygiene practice, with the lowest rates observed before patient interaction, which, together with the variability recorded across departments, underscores the challenges involved in achieving a uniform level of compliance. Hence, additional training is essential to raise awareness among healthcare workers, while repeating this survey over time will also be crucial, so that hand hygiene compliance can be monitored and any major issue identified.
Introduction: Non-italian citizens experienced less access to anti-COVID-19 vaccination, compared to the native population. Literature has found differences in adherence to anti-COVID-19 vaccination among these groups; however, there are apparently no studies that investigated the role of citizenship. Our objective was to investigate the role of citizenship in vaccine hesitancy toward anti-COVID-19 vaccination and the completion of vaccine cycle, in the non-Italian citizens resident in the Umbria Region.
Study design: This is a population study, performed on resident population in Umbria.
Methods: Population data were obtained thanks to a record linkage between the Regional Health Information System and the regional DBCOVID Umbria database. On this dataset, a descriptive and logistic regression analyses were performed.
Results: The 19.2% of non-Italian citizens did not take even one dose, 2.1% did not complete it and 40.6% did not take the additional dose. The range of values of which these results are an average, however, is very wide, suggesting important differences in COVID-19 vaccine up taking, among different citizenships. The logistic regression shows that citizenships with the highest probability of non-adherence to vaccination, compared to Philippine, was Romanian (OR=7.8), followed by Macedonian (OR=7.3) and Polish (OR=5.9).
Conclusions: The study provides evidence of differences among citizenships that pinpoint the importance of understanding the reasons behind these behaviours, to support decisions around health policies tailored to each citizenship.
Background: The changes in health, social and demographic needs impose new approaches to cures and care without giving up patients' safety. Although several studies analysed the patient safety approach and strategies, the literature considering the home care setting seems still scarce. The analysis of the phenomenon of medication errors in the primary care setting highlights the necessity of exploring the specific variables to understand how to prevent or reduce the occurrence of a medication error in the home context. This review investigates the main preventive strategies implemented at the patient's home to prevent and/or limit the possibility of a medication error.
Design: The scoping review was conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) statement and based on the guidelines of the Joanna Briggs Institute.
Methods: No time or language limit was set to obtain the most comprehensive results possible. The following databases were queried: PubMed, Cochrane, CINAHL, ERIC and PsycINFO via EBSCO. All literature published up to 31 December 2022 was considered for data collection.
Results: The main preventive strategies implemented in the patient's home to prevent a medication error are: Multidisciplinary teams, therapeutic reconciliation and computerised systems that improve information sharing. As evidenced by all of the included studies, no educational intervention or preventive strategy individually reduces the risk of making a medication error.
Conclusions: It would be desirable for healthcare professionals to be constantly updated about their knowledge and understand the importance of introducing the aforementioned preventive strategies to guarantee safe care that protects the person from medication errors even at the patient's home.
Background: Scientific evidence demonstrates that poor sleep quality can lead to various health problems. This study aimed to investigate sleep patterns among Italian university students and identify several factors that may contribute to its quality.
Study design: Cross-sectional study.
Methods: An electronic questionnaire regarding sociodemographic characteristics, lifestyle, and sleep-related habits, including the Pittsburgh Sleep Quality Index (PSQI) questionnaire, was distributed between January 2022 and July 2023 among students belonging to 12 universities located in Northern, Central, and Southern Italy.
Results: On a total of 1,674 questionnaires collected, the participants (mean age 24.06±4.56 years, 71.3% F) reported an average number of hours of nocturnal sleep equal to 6.89±1.28 hours. A total of 927 (54.6%) of respondents showed a poor sleep quality (PSQI >5). Regression analysis showed that better sleep quality is associated with lower age, attending universities in Northern Italy, less time spent on electronic devices during the day, not being used to study at night and not playing videogames before sleep.
Conclusion: From a public health perspective, our findings suggest that public health operators should raise the awareness of young adults about the importance of sleep quality for maintaining good health, as well as the impact that certain behaviors can have on sleep.
Objective: The COVID-19 pandemic has disrupted educational systems worldwide, raising concerns about its impact on academic performance, particularly among developmental age students.
Methods: A systematic review with meta-analysis aimed to evaluate the association between the COVID-19 pandemic and the academic performance in this population was performed according to PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus and Embase were searched on December 2023 to identify relevant studies. Both fixed and random effect models were performed. The Effect size was reported as Cohen's d with a 95% Confidence Interval. Studies' quality was assessed using the Newcastle-Ottawa scale. The protocol was registered in PROSPERO.
Results: A total of 30 studies met the inclusion criteria, but only 13 could be combined in the meta-analysis. Based on a sample size of 4,893,499 students, pooled Cohen's d was -0.07 [(95% CI = -0.10; -0.03); p-value <0.001]. Subgroup analyses by subject suggested that performance in math was affected the most, Cohen's d= -0.14 [(-0.18; -0.10); p-value <0.001].
Conclusion: The findings revealed a significant negative association between the COVID-19 pandemic and academic performance among developmental age students. Interventions to mitigate the adverse effects of the pandemic on educational outcomes in this population are needed.
Introduction: During the COVID-19 Pandemic, the use of digital devices during work activities has increased with important repercussions on the psychological and physical well-being of the employees. The aim of this study was to investigate the prevalence of musculoskeletal and visual disorders related to the use of computers and home workstation.
Methods: The study is a cross-sectional study. A checklist, from the National Institute of Health, was administered to white collar workers of a large international pharmaceutical company based in Italy.
Results: Our study showed that postural breaks have a protective effect on neck/shoulder pain (OR 0.32, CI 0.16-0.62), back and lower extremity pain (OR 0.35, CI 0.18-0.69), and eye burning (OR 0.50, CI 0.27-0.94) of study participants.
Conclusions: The research recommends that remote employees who often change their workstations should establish a suitable work environment and obtaining enough risk training from an occupational physician. This is essential for maintaining their mental and physical well-being.
Introduction: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers.
Study design: The aim of the study is to standardize a method for predicting the risk of Legionella contamination in the water supply of a hospital facility, by comparing Machine Learning, conventional and combined models.
Methods: During the period July 2021- October 2022, water sampling for Legionella detection was performed in the rooms of an Italian hospital pavilion (89.9% of the total number of rooms). Fifty-eight parameters regarding the structural and environmental characteristics of the water network were collected. Models were built on 70% of the dataset and tested on the remaining 30% to evaluate accuracy, sensitivity, and specificity.
Results: A total of 1,053 water samples were analyzed and 57 (5.4%) were positive for Legionella. Of the Machine Learning models tested, the most efficient had an input layer (56 neurons), hidden layer (30 neurons), and output layer (two neurons). Accuracy was 93.4%, sensitivity was 43.8%, and specificity was 96%. The regression model had an accuracy of 82.9%, sensitivity of 20.3%, and specificity of 97.3%. The combination of the models achieved an accuracy of 82.3%, sensitivity of 22.4%, and specificity of 98.4%. The most important parameters that influenced the model results were the type of water network (hot/cold), the replacement of filter valves, and atmospheric temperature. Among the models tested, Machine Learning obtained the best results in terms of accuracy and sensitivity.
Conclusions: Future studies are required to improve these predictive models by expanding the dataset using other parameters and other pavilions of the same hospital.
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