The Metaphor of Circle: Stanislavski, Phenomenology of Roundness and High Yoga

Stanislavski’s Eastern influence and his persistent fascination with Yoga is an established area of research. Stanislavski has two major yogic influences in the System: Hatha Yoga and Raja Yoga. The first is about training the body through postures, movements, stances and breathing exercises to make it active, relaxed and energetic. The second includes a range of exclusive practices on concentration and visualization that are an integral part of the actor’s creativity. However, in the current research, there is a clear lack of a comprehensive practical method that informs Stanislavski’s use of yogic practice in the System: what specific yogic exercises were used and what synergies were developed in the training? Scholars have, so far, only explored Stanislavski’s literary legacy of his encounter with yoga, identifying the Sanskrit terms appeared in his writings such as prana for breathing and the Indian anecdotes he used when answering the questions of his students. Focusing on the metaphor of the circle, the phenomenology of roundness and the Deleuzian notions of ‘the invisible forces of sensations’ and ‘the body becomes an event itself’, the present study, for the first time, offers useful insights into the fundamental principles of yoga and their methods of practice as a way of developing a new training to understand the significance of Stanislavski in the 21st century

Weekend admission to hospital has a higher risk of death in the elective setting than in the emergency setting: a retrospective database study of national health service hospitals in England

<p>Abstract</p> <p>Background</p> <p>Although acute hospitals offer a twenty-four hour seven day a week service levels of staffing are lower over the weekends and some health care processes may be less readily available over the weekend. Whilst it is thought that emergency admission to hospital on the weekend is associated with an increased risk of death, the extent to which this applies to elective admissions is less well known. We investigated the risk of death in elective and elective patients admitted over the weekend versus the weekdays.</p> <p>Methods</p> <p>Retrospective statistical analysis of routinely collected acute hospital admissions in England, involving all patient discharges from all acute hospitals in England over a year (April 2008-March 2009), using a logistic regression model which adjusted for a range of patient case-mix variables, seasonality and admission over a weekend separately for elective and emergency (but excluding zero day stay emergency admissions discharged alive) admissions.</p> <p>Results</p> <p>Of the 1,535,267 elective admissions, 91.7% (1,407,705) were admitted on the weekday and 8.3% (127,562) were admitted on the weekend. The mortality following weekday admission was 0.52% (7,276/1,407,705) compared with 0.77% (986/127,562) following weekend admission. Of the 3,105,249 emergency admissions, 76.3% (2,369,316) were admitted on the weekday and 23.7% (735,933) were admitted on the weekend. The mortality following emergency weekday admission was 6.53% (154,761/2,369,316) compared to 7.06% (51,922/735,933) following weekend admission. After case-mix adjustment, weekend admissions were associated with an increased risk of death, especially in the elective setting (elective Odds Ratio: 1.32, 95% Confidence Interval 1.23 to 1.41); vs emergency Odds Ratio: 1.09, 95% Confidence Interval 1.05 to 1.13).</p> <p>Conclusions</p> <p>Weekend admission appears to be an independent risk factor for dying in hospital and this risk is more pronounced in the elective setting. Given the planned nature of elective admissions, as opposed to the unplanned nature of emergency admissions, it would seem less likely that this increased risk in the elective setting is attributable to unobserved patient risk factors. Further work to understand the relationship between weekend processes of care and mortality, especially in the elective setting, is required.</p

Global quantitative indices reflecting provider process-of-care: data-base derivation

Background: Controversy has attended the relationship between risk-adjusted mortality and process-of-care. There would be advantage in the establishment, at the data-base level, of global quantitative indices subsuming the diversity of process-of-care. Methods: A retrospective, cohort study of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 1993-2003, at the level of geographic and ICU-level descriptors (n = 35), for both hospital survivors and non-survivors. Process-of-care indices were established by analysis of: (i) the smoothed time-hazard curve of individual patient discharge and determined by pharmaco-kinetic methods as area under the hazard-curve (AUC), reflecting the integrated experience of the discharge process, and time-to-peak-hazard (TMAX, in days), reflecting the time to maximum rate of hospital discharge; and (ii) individual patient ability to optimize output (as length-of-stay) for recorded data-base physiological inputs; estimated as a technical production-efficiency (TE, scaled [0,(maximum)1]), via the econometric technique of stochastic frontier analysis. For each descriptor, multivariate correlation-relationships between indices and summed mortality probability were determined. Results: The data-set consisted of 223129 patients from 99 ICUs with mean (SD) age and APACHE III score of 59.2(18.9) years and 52.7(30.6) respectively; 41.7% were female and 45.7% were mechanically ventilated within the first 24 hours post-admission. For survivors, AUC was maximal in rural and for-profit ICUs, whereas TMAX (≥ 7.8 days) and TE (≥ 0.74) were maximal in tertiary-ICUs. For non-survivors, AUC was maximal in tertiary-ICUs, but TMAX (≥ 4.2 days) and TE (≥ 0.69) were maximal in for-profit ICUs. Across descriptors, significant differences in indices were demonstrated (analysisof- variance, P ≤ 0.0001). Total explained variance, for survivors (0.89) and non-survivors (0.89), was maximized by combinations of indices demonstrating a low correlation with mortality probability. Conclusions: Global indices reflecting process of care may be formally established at the level of national patient databases. These indices appear orthogonal to mortality outcome.John L Moran, Patricia J Solomon and the Adult Database Management Committee (ADMC) of the Australian and New Zealand Intensive Care Society (ANZICS

What is the empirical evidence that hospitals with higher-risk adjusted mortality rates provide poorer quality care? A systematic review of the literature

<p>Abstract</p> <p>Background</p> <p>Despite increasing interest and publication of risk-adjusted hospital mortality rates, the relationship with underlying quality of care remains unclear. We undertook a systematic review to ascertain the extent to which variations in risk-adjusted mortality rates were associated with differences in quality of care.</p> <p>Methods</p> <p>We identified studies in which risk-adjusted mortality and quality of care had been reported in more than one hospital. We adopted an iterative search strategy using three databases – Medline, HealthSTAR and CINAHL from 1966, 1975 and 1982 respectively. We identified potentially relevant studies on the basis of the title or abstract. We obtained these papers and included those which met our inclusion criteria.</p> <p>Results</p> <p>From an initial yield of 6,456 papers, 36 studies met the inclusion criteria. Several of these studies considered more than one process-versus-risk-adjusted mortality relationship. In total we found 51 such relationships in a widen range of clinical conditions using a variety of methods. A positive correlation between better quality of care and risk-adjusted mortality was found in under half the relationships (26/51 51%) but the remainder showed no correlation (16/51 31%) or a paradoxical correlation (9/51 18%).</p> <p>Conclusion</p> <p>The general notion that hospitals with higher risk-adjusted mortality have poorer quality of care is neither consistent nor reliable.</p

Performer training: researching practice in the theatre laboratory

Examination of the issues and challenges of workshop and laboratory training as the subject and object of research. Includes case studies of Meyerhold, Lecoq, Halprin and 'Floorplan'

Did hospital mortality in England change from 2005 to 2010? A retrospective cohort analysis

Background: There is some evidence that hospital performance in England measured by the Dr Foster Hospital Standardised Mortality Ratio (HSMR) has improved substantially over the last 10 years. This study explores mortality in-hospital and up to 30 days post-discharge over a five year period to determine whether there have been improvements in case-mix adjusted mortality, to examine if any changes are due to changes in case-mix adjustment variables such as age, sex, method of admission and comorbidity, and to compare changes between hospital trusts. Methods: Using Hospital Episode Statistics linked to mortality data from the Office for National Statistics the Summary Hospital-Level Mortality Index (SHMI) was calculated for all patients who were discharged or died in general acute hospital trusts in England for the period 01/04/2005 to 30/09/2010. Results: During this five year period the number of admissions rose by 8% but deaths fell by 5%. The SHMI fell by 24% from 112 to 85 over the period, partly due to fewer deaths but partly due to increasing numbers predicted by the SHMI model. Excluding comorbidities from the model the SHMI fell by 18% from 108 to 89 over this period. The reduction was similar in emergency and elective admissions and in all other sub-groups examined. The average quarterly change in SHMI varied considerably between trusts (range: -4.4 to −0.2). Conclusions: As measured by the SHMI there has been a 24% improvement in mortality in acute general trusts in England over a period of five and a half years. Part of this improvement is an artificial effect caused by changes in the depth of coding of comorbidities and other effects due to change in case-mix or non-constant risk