Interaction Rituals and Jumbled Emotions Among “Relative Strangers”: Simulated Patient Work on a Trainee Complementary Therapy Practitioner Program

Learning games such as role-play (which we refer to as “simulated interaction rituals”) are commonly used as social tools to develop trainee health practitioners. However, the effect of such rituals on individual and group participant emotions has not been carefully studied. Using a heuristic approach, we explore the experiences of complementary therapy practitioner trainees (and their trainers) participating in a personal development course. Ten trainees and two tutors were interviewed, observational notes taken, and a secondary qualitative analysis undertaken. Participants and tutors described a medley of disparate emotional and moral responses to group rituals, conceptualized in this article as “jumbled emotions.” Such emotions required disentangling, and both trainees and staff perceived participating in unfamiliar rituals “with relative strangers” as challenging. Front of stage effects are frequently processed “backstage,” as rituals threaten social embarrassment and confusion. Concerns around emotional triggers, authenticity, and outcomes of rituals arise at the time, yet trainees can find ways to work through these issues in time.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.