Nurses are accountable to apply the nursing process, which is key for patient care: It is a problem-solving process providing the structure for care plans and documentation. The state-of-the art nursing process is based on classifications that contain standardized concepts, and therefore, it is named Advanced Nursing Process. It contains valid assessments, nursing diagnoses, interventions, and nursing-sensitive patient outcomes. Electronic decision support systems can assist nurses to apply the Advanced Nursing Process. However, nursing decision support systems are missing, and no "gold standard" is available. The study aim is to develop a valid Nursing Process-Clinical Decision Support System Standard to guide future developments of clinical decision support systems. In a multistep approach, a Nursing Process-Clinical Decision Support System Standard with 28 criteria was developed. After pilot testing (N = 29 nurses), the criteria were reduced to 25. The Nursing Process-Clinical Decision Support System Standard was then presented to eight internationally known experts, who performed qualitative interviews according to Mayring. Fourteen categories demonstrate expert consensus on the Nursing Process-Clinical Decision Support System Standard and its content validity. All experts agreed the Advanced Nursing Process should be the centerpiece for the Nursing Process-Clinical Decision Support System and should suggest research-based, predefined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions, and patient outcomes.
ObjectiveTo develop, implement and evaluate a personalized patient discharge letter (PPDL) to improve the quality of handoff communication from hospital to home.DesignFrom the end of 2006–09 we conducted a quality improvement project; consisting of a before–after evaluation design, and a process evaluation.SettingFour general internal medicine wards, in a 1024-bed teaching hospital in Amsterdam, the Netherlands.ParticipantsAll consecutive patients of 18 years and older, admitted for at least 48 h.InterventionsA PPDL, a plain language handoff communication tool provided to the patient at hospital discharge.Main Outcome MeasuresVerbal and written information provision at discharge, feasibility of integrating the PPDL into daily practice, pass rates of PPDLs provided at discharge.ResultsA total of 141 patients participated in the before–after evaluation study. The results from the first phase of quality improvement showed that providing patient with a PPDL increased the number of patients receiving verbal and written information at discharge. Patient satisfaction with the PPDL was 7.3. The level of implementation was low (30%). In the second phase, the level of implementation improved because of incorporating the PPDL into the electronic patient record (EPR) and professional education. An average of 57% of the discharged patients received the PPDL upon discharge. The number of discharge conversations also increased.ConclusionPatients and professionals rated the PPDL positively. Key success factors for implementation were: education of interns, residents and staff, standardization of the content of the PPDL, integrating the PPDL into the electronic medical record and hospital-wide policy.
Despite growing popular interest for the mental health of electronic music artists, scientific research addressing this topic has remained largely absent. As such, the aim of the current study was to examine the mental health of electronic music artists, as well as a number of determinants. Using a cross-sectional quantitative design, a total of 163 electronic music artists participated in this study. In line with the two-continua model of mental health, both symptoms of depression/anxiety and well-being were adopted as indicators for mental health. Furthermore, standardized measures were used to assess potential determinants of mental health, including sleep disturbance, music performance anxiety, alcohol abuse, drug abuse, occupational stress, resilience, and social support. Results highlighted that around 30% of participants experienced symptoms of depression/anxiety. Nevertheless, the majority of these participants still demonstrated at least moderate levels of functioning and well-being. Sleep disturbance formed a significant predictor for both symptoms of depression/anxiety and well-being. Furthermore, resilience and social support were significant predictors for well-being. The results provide a first glimpse into the mental health challenges experienced by electronic music artists and support the need for increased research as well as applied initiatives directed at safeguarding their mental health.
