Conectar
This study explored the knowledge and confidence levels of nursing academics in teaching both the theories and practical skills of digital health in undergraduate nursing programs.
A cross-sectional study.
A structured online survey was distributed among nursing academics across Australian universities. The survey included two sections: (1) the participants' demographics and their nursing and digital health teaching experience; (2) likert scales asking the participants to rate their knowledge and confidence to teach the theories and practical skills of four main themes; digital health technologies, information exchange, quality and digital professionalism.
One hundred and nineteen nursing academics completed part one, and 97 individuals completed part two of the survey. Only 6% (n = 5) of the participants reported having formal training in digital health. Digital health was mainly taught as a module (n = 57, 45.9%), and assessments of theory or practical application of digital health in the nursing curriculum were uncommon, with 79 (69.9%) responding that there was no digital health assessment in their entry to practice nursing programs. Among the four core digital health themes, the participants rated high on knowledge of digital professionalism (22.4% significant knowledge vs. 5.9% no knowledge) but low on information exchange (30% significant knowledge vs. 28.3% no knowledge). Statistically significant (p < .001) associations were found between different themes of digital health knowledge and the level of confidence in teaching its application. Nursing academics with more than 15 years of teaching experience had a significantly higher level of knowledge and confidence in teaching digital health content compared with those with fewer years of teaching experience.
There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Nursing academics need to upskill in digital health to prepare the future workforce to be capable in digitally enabled health care settings.
Nursing academics have a limited level of digital knowledge and confidence in preparing future nurses to work in increasingly technology-driven health care environments. Addressing this competency gap and providing sufficient support for nursing academics in this regard is essential.
What problem did the study address? Level of knowledge and confidence among nursing academics to teach digital health in nursing practice. What were the main findings? There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Where and on whom will the research have an impact? Professional nursing education globally.
The STROBE guideline was used to guide the reporting of the study.
The call for participation from nursing academics across Australia provided an introductory statement about the project, its aim and scope, and the contact information of the principal researcher. A participant information sheet was shared with the call providing a detailed explanation of participation. Nursing academics across Australia participated in the survey through the link embedded in the participation invite.
Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.
We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.
We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.
5241 patients were included in the analysis. For ICU days 2–7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4–7 for all states.
We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a
by Sacha Parada, Bérengère Rubio, Elsa Taschini, Xavier Laqueille, Malika El Youbi, Pierre Paris, Bernard Angerville, Alain Dervaux, Jean-François Verlhiac, Eve Legrand
This work aimed at creating a psychosocial intervention based on growth mindset theory and implementation intention strategies, in order to reduce alcohol consumption among users in the general population, and the clinical population of individuals with alcohol use disorder. A mixed method approach was used, combining qualitative and quantitative research methods among both populations. Four focus groups were first conducted to extract arguments in favor of a malleable view of alcohol consumption (study 1A), situations that trigger the desire to drink alcohol, as well as strategies used by people to counteract this need (study 1B). Data were analyzed using reflective thematic analysis in line with the scientific literature on alcohol consumption. The results were used to create a questionnaire scoring the relevance of each argument, situation and strategy (study 2). The 20 best scored arguments, situations and strategies were selected to create the intervention. The created intervention consisted in a popularized scientific article describing alcohol consumption as malleable, including the selected arguments and followed by two internalization exercises. Then, a volitional help sheet included the selected situations and solutions was presented, allowing forming up to three plans. The discussion focused on the added value of the created material compared to pre-existing tools in the literature, and presents plans to test the intervention in a future study.
FreshRSS 