Prenatal Substance Use and Perceptions of Parent and Partner Use Using the 4P’s Plus Screener

Maternal and Child Health Journal - Background Prenatal substance use screening is recommended. The 4 P’s Plus screener includes questions on perceived problematic substance use in parents...     

Action research methodology in clinical pharmacy: how to involve and change

Introduction The focus in clinical pharmacy practice is and has for the last 30–35 years been on changing the role of pharmacy staff into service orientation and patient counselling. One way of doing this is by involving staff in change process and as a researcher to take part in the change process by establishing partnerships with staff. On the background of the authors’ widespread action research (AR)-based experiences, recommendations and comments for how to conduct an AR-study is described, and one of their AR-based studies illustrate the methodology and the research methods used. Methodology AR is defined as an approach to research which is based on a problem-solving relationship between researchers and clients, which aims at both solving a problem and at collaboratively generating new knowledge. Research questions relevant in AR-studies are: what was the working process in this change oriented study? What learning and/or changes took place? What challenges/pitfalls had to be overcome? What were the influence/consequences for the involved parts? When to use If you want to implement new services and want to involve staff and others in the process, an AR methodology is very suitable. The basic advantages of doing AR-based studies are grounded in their participatory and democratic basis and their starting point in problems experienced in practice. Limitations Some of the limitations in AR-studies are that neither of the participants in a project steering group are the only ones to decide. Furthermore, the collective process makes the decision-making procedures relatively complex.

     

Use of a Checklist During Observation of a Simulated Cardiac Arrest Scenario Does Not Improve Time to CPR and Defibrillation Over Observation Alone for Subsequent Scenarios

Theory: Immersive simulation is a common mode of education for medical students. Observation of clinical simulations prior to participation is believed to be beneficial, though this is often a passive process. Active observation may be more beneficial. Hypotheses: The hypothesis tested in this study was that the active use of a simple checklist during observation of an immersive simulation would result in better participant performance in a subsequent scenario compared with passive observation alone. Methods: Medical students were randomized to either passive or active (with checklist) observation of an immersive simulation involving cardiac arrest prior to participating in their own simulation. Performance measures included time to cardiopulmonary resuscitation (CPR) and time to defibrillation and were compared between first and second scenarios as well as between passive and active observers. Results: Seventy-nine simulations involving 232 students were conducted. Mean time to CPR was 18 seconds (SD = 11.6) for those using the checklist and 24 seconds (SD = 15.8) for those who observed passively (M difference = 6 seconds), t(35) = 1.46, p =.153. Time to defibrillation was 94 seconds (SD = 26.4) for those using the checklist and 92 seconds (SD = 23.8) for those who observed passively (M difference = –2 seconds), t(38) =.21, p =.837. Time to CPR was 24 seconds (SD = 15.8) for passive observers and 31 seconds (SD = 21.0; M difference = 7 seconds), t(35) = 1.13, p =.265, for their first scenario counterparts. Time to CPR was 18 seconds (SD = 11.6) for active observers and 36 seconds (SD = 26.2; M difference = 18 seconds), t(24) = 2.81, p =.010, for their first scenario counterparts. Time to defibrillation was 92 seconds (SD = 23.8) for passive observers and 125 seconds (SD = 32.2; M difference = 33 seconds), t(33) = 3.63, p =.001, for their first scenario counterparts. Time to defibrillation was 94 seconds (SD = 26.4) for the active observers and 132 seconds (SD = 52.9; M difference = 38 seconds), t(28) =.46, p =.008, for their first scenario counterparts. Conclusions: Observation alone leads to improved performance in the management of a simulated cardiac arrest. The active use of a simple skills-based checklist during observation did not appear to improve performance over passive observation alone.     

Prevalence and Predictors of Quality of Recovery at Home After Day Surgery

Traditionally, major complications and unanticipated admission/readmission rates were used to assess outcome after day surgery. However, in view of the relative absence of major complications the quality of recovery (QOR) should be considered one of the principal endpoints after day surgery. In our study, the level of QOR is defined by a combination of the Global Surgical Recovery (GSR) Index and the Quality of Life (QOL).The aim of this study was to analyze prevalence and predictors of QOR after day surgery on the fourth postoperative day.Elective patients scheduled for day surgery from November 2008 to April 2010 were enrolled in a prospective cohort study. Outcome parameters were measured by using questionnaire packages at 2 time points: 1 week preoperatively and 4 days postoperatively. Primary outcome parameter is the QOR and is defined as good if the GSR index >80% as well as the postoperative QOL is unchanged or improved as compared with baseline. QOR is defined as poor if both the GSR index ≤80% and if the postoperative QOL is decreased as compared with baseline. QOR is defined as intermediate in all other cases. Three logistic regression analyses were performed to determine predictors for poor QOR after day surgery.A total of 1118 patients were included. A good QOR was noted in 17.3% of patients, an intermediate QOR in 34.8%, and a poor QOR in 47.8% 4 days after day surgery. The best predictor for poor QOR after day surgery was type of surgery. Other predictors were younger age, work status, and longer duration of surgery. A history of previous surgery, expected pain (by the patient) and high long-term surgical fear were significant predictors of poor QOR in only 1 of 3 prediction models.The QOR at home 4 days after day surgery was poor in the majority of patients and showed a significant procedure-specific variation. Patients at risk for poor QOR can be identified during the preoperative period based on type of surgery, age, work status, and the duration of the surgery.