Factors associated with evidence-based practice among registered nurses in Sweden : a national cross-sectional study

Background: Evidence-based practice (EBP) is emphasized to increase the quality of care and patient safety. EBP is often described as a process consisting of distinct activities including, formulating questions, searching for information, compiling the appraised information, implementing evidence, and evaluating the resulting practice. To increase registered nurses' (RNs') practice of EBP, variables associated with such activities need to be explored. The aim of the study was to examine individual and organizational factors associated with EBP activities among RNs 2 years post graduation. Methods: A cross-sectional design based on a national sample of RNs was used. Data were collected in 2007 from a cohort of RNs, included in the Swedish Longitudinal Analyses of Nursing Education/Employment study. The sample consisted of 1256 RNs (response rate 76%). Of these 987 RNs worked in healthcare at the time of the data collection. Data was self-reported and collected through annual postal surveys. EBP activities were measured using six single items along with instruments measuring individual and work-related variables. Data were analyzed using logistic regression models. Results: Associated factors were identified for all six EBP activities. Capability beliefs regarding EBP was a significant factor for all six activities (OR = 2.6 - 7.3). Working in the care of older people was associated with a high extent of practicing four activities (OR = 1.7 - 2.2). Supportive leadership and high collective efficacy were associated with practicing three activities (OR = 1.4 - 2.0). Conclusions: To be successful in enhancing EBP among newly graduated RNs, strategies need to incorporate both individually and organizationally directed factors.

Modeling Hospital Readmissions in Dalarna County, Sweden

Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

The importance of patient involvement in stroke rehabilitation

OBJECTIVE: To investigate the perceived needs for health services by persons with stroke within the first year after rehabilitation, and associations between perceived impact of stroke, involvement in decisions regarding care/treatment, and having health services needs met. METHOD: Data was collected, through a mail survey, from patients with stroke who were admitted to a university hospital in 2012 and had received rehabilitation after discharge from the stroke unit. The rehabilitation lasted an average of 2 to 4.6 months. The Stroke Survivor Needs Survey Questionnaire was used to assess the participants' perceptions of involvement in decisions on care or treatment and needs for health services in 11 problem areas: mobility, falls, incontinence, pain, fatigue, emotion, concentration, memory, speaking, reading, and sight. The perceived impact of stroke in eight areas was assessed using the Stroke Impact Scale (SIS) 3.0. Eleven logistic regression models were created to explore associations between having health services needs met in each problem area respectively (dependent variable) and the independent variables. In all models the independent variables were: age, sex, SIS domain corresponding to the dependent variable, or stroke severity in cases when no corresponding SIS domain was identified, and involvement in decisions on care and treatment. RESULTS: The 63 participants who returned the questionnaires had a mean age of 72 years, 33 were male and 30 were female. Eighty percent had suffered a mild stroke. The number of participants who reported problems varied between 51 (80%, mobility) and 24 (38%, sight). Involvement in decisions on care and treatment was found to be associated with having health services needs met in six problem areas: falls, fatigue, emotion, memory, speaking, and reading. CONCLUSIONS: The results highlight the importance of involving patients in making decisions on stroke rehabilitation, as it appears to be associated with meeting their health services needs.