Effectiveness of interventions to reduce occupational stress among emergency department staff: a systematic review protocol

Experimental Mindfulness Intervention in an Emergency Department for Stress Management and Development of Positive Working Environment

Mindfulness-based interventions have increasingly gained the interest of health professionals in the last decade, especially practices that are short, economical, easily accessible, and physically, cognitively, and psychologically compelling. Nurses of Emergency Departments are a special, dynamic, but at the same time vulnerable group of health professionals who work in shifts and face multiple challenges. Considering the recent literature and the fact that stress and a hostile work environment are the top ranked health professionals’ challenges, there is a need for a further study of the use of mindfulness. This study aimed to investigate the effect of the application of mindfulness on nurses in the Emergency Department on several factors related to daily nursing practice and that directly affect these specific health professionals. This experimental study was performed on 14 participating nurses in the Emergency Department of a Public General Hospital in Athens, randomized into two groups: a control and an intervention group. The data collection tools were two digital smart devices, participatory observation, and semi-structured interviews. By practicing mindfulness meditation, the participating nurses in the intervention group showed improvement in their cognitive functions (attention, thinking, memory, concentration), professional interpersonal relationships, personal satisfaction and communication with patients and caregivers, sleep rate, negative emotions, and behaviors. The findings suggest that the application of mindfulness practices should be considered an easy, affordable, economical, accessible, and effective method that nurses can use to strengthen and empower themselves, enjoying its multiple benefits. The effectiveness of the application of mindfulness remains an important issue for future research in other health professionals as well.

The effect of perceived stress on organizational silence in emergency service doctors in Turkey: The mediating role of emotional intelligence

Objectives

The purpose of this study is to determine the role of emotional intelligence in the relationship between the stress perceptions of emergency medicine doctors and their organizational silence behaviors.

Methods

Data were collected digitally from 434 doctors working in emergency departments in Turkey. On the assumption that perceived stress was effective on organizational silence behavior and that emotional intelligence mediates this relationship, hypotheses were developed and a mediating effect model was established. The research model and hypotheses were shaped through Structural Equation Modeling (SEM). Hayes 4th model was used to test the research hypotheses. The research model was tested via SPSS Process v4.1 by Andrew F. Hayes.

Results

According to the correlation analysis to determine the relationship between the variables, it was determined that perceived stress was positively correlated with organizational silence behavior, emotional intelligence was negatively correlated with perceived stress, and emotional intelligence was negatively correlated with organizational silence. As a result of the mediating effect model test, it was determined that emotional intelligence had a statistically significant mediating effect in the effect of perceived stress on organizational silence.

Conclusion

Within the framework of the findings, it is thought that emotional intelligence is a key variable in turning the negative energy between stress and silence into positive.

A systematic review of interventions aiming to improve newly-qualified doctors' wellbeing in the United Kingdom

Background

Newly-qualified doctors in the United Kingdom experience a great deal of stress and have poor wellbeing when compared to more senior counterparts. A number of interventions have been put in place to boost healthcare professionals’ wellbeing, but little is known about interventions aimed to improve the wellbeing of newly-qualified doctors in the United Kingdom. This study aims to systematically review current evidence of interventions which improved the wellbeing of newly-qualified junior doctors in the United Kingdom.

Methods

Five key electronic databases were searched. Subsequently, reference scanning and citation search was performed. Studies were included if they were conducted from the commencement of the Foundation Programme in 2004, until 2019. In addition, studies had to be performed on junior doctors: working in the United Kingdom and within their first five years post-qualification and have a quantitative outcome. Studies which did not meet these criteria were excluded. Quality was assessed using the modified Newcastle-Ottawa scale. Bias was not formally assessed using a standardised tool.

Results

Seven papers met the inclusion criteria and identified three main types of interventions: mentorship, mindfulness and clinical preparation interventions. The majority of included studies reported a positive result from the performed intervention, suggesting these to be beneficial in improving junior doctor wellbeing, and thereby reducing anxiety and stress levels. However, most of the studies used small sample sizes.

Conclusions

This review reveals that there is dearth of evidence on the effectiveness of intervention to improve the wellbeing of newly-qualified doctors in the United Kingdom. Most of the identified interventions focused on relieving stress and anxiety inherent within newly-qualified doctors’ training programmes. However, wellbeing interventions need to take into cognisance all the factors which impact on wellbeing, particularly job-related factors. We recommend that future researchers implement large-scale holistic interventions using appropriate research methods.

Systematic review registration: PROSPERO CRD42019127341.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-022-00868-8.

Internet-based cognitive behavioural therapy programme with and without videoconference guidance sessions: A randomized controlled trial to treat work-related symptoms of anxiety and depression

This study provides the results from the implementation of a highly structured therapist-guided iCBT programme for people with work-related anxiety and depression, in terms of programme efficacy, participants' adherence and satisfaction. Seventy-seven national police workers were randomly allocated to one of two groups: without additional videoconference sessions (web platform with guidance of therapist) and with additional videoconference sessions (same intervention as the previous group, plus two videoconference guidance sessions with a psychologist). The intervention was composed of 12 sessions and took place for 17-20 weeks. We found an adherence rate of 36.4%, with no differences between groups. All participants endorsed lower depression (BDI-II F(1) = 36.98, p < .001; ATQ F(1) = 24.22, p < .001), and anxiety (STAI-State F(1) = 76.62, p < .001) after the programme. As a variable related to anxiety and depression in workplace, participants also showed higher assertiveness levels (RAS F(1) = 8.96, p < .001). A significant reduction of the mean level of anxiety perceived by participants as the intervention programme progressed was observed in both groups (F(2) = 7.44; p = .003). Participants were satisfied with the therapists' intervention and with the programme. No significant group effects were found for any of the measures. Reduction in depression levels was maintained in the 12-month follow-up, but levels of anxiety increased. This study is innovative, as it is the first controlled trial to analyse the effect of two added videoconference sessions, and it includes short- and long-term measures, which is not usual. The results are discussed to clarify the role of the contact with the therapist to improve treatment adherence.

Man vs. machine: Predicting hospital bed demand from an emergency department

Background

The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems.

Objective

Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED.

Methods

This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC).

Results

All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77–0.87], 0.80 (95% CI: 0.75–0.85), 0.76 (95% CI: 0.71–0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR.

Conclusions

Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task.