‘What’s Up Doc?’ An Exploratory Study Investigating the Emotional Labour and its Potential Impact on the Emotional Wellbeing on General Practitioners in a Primary Care Setting

General Practitioners examine, diagnose and advise unwell patients, they also provide emotional labour during their patient interactions. The purpose of this study was to explore the emotional labour that General Practitioners experience during their patient interactions and to understand the impact that it has on their emotional wellbeing. The study utilised the Conservation of Resources theory (Höbfoll, 1989), and the Job-Demand Resource theory (Demerouti and Bakker, 2011) to examine the links between emotional labour and job demands with emotional wellbeing. This study conducted 16 semi-structured interviews with General Practitioners in a Primary Care setting and found that emotional labour does have a negative impact on their emotional wellbeing, and that surface acting was the preferred method of emotional labour, as deep acting was found to increase burnout as it was more exhausting to achieve. The study also found that job demands, and emotional labour together exacerbate emotional wellbeing and burnout and is linked with General Practitioners intentions to leave the profession entirely. The study has also highlighted the lack of awareness that exists about emotional labour and well-being

‘What’s Up Doc?’ An Exploratory Study Investigating the Emotional Labour and its Potential Impact on the Emotional Wellbeing on General Practitioners in a Primary Care Setting

General Practitioners examine, diagnose and advise unwell patients, they also provide emotional labour during their patient interactions. The purpose of this study was to explore the emotional labour that General Practitioners experience during their patient interactions and to understand the impact that it has on their emotional wellbeing. The study utilised the Conservation of Resources theory (Höbfoll, 1989), and the Job-Demand Resource theory (Demerouti and Bakker, 2011) to examine the links between emotional labour and job demands with emotional wellbeing. This study conducted 16 semi-structured interviews with General Practitioners in a Primary Care setting and found that emotional labour does have a negative impact on their emotional wellbeing, and that surface acting was the preferred method of emotional labour, as deep acting was found to increase burnout as it was more exhausting to achieve. The study also found that job demands, and emotional labour together exacerbate emotional wellbeing and burnout and is linked with General Practitioners intentions to leave the profession entirely. The study has also highlighted the lack of awareness that exists about emotional labour and well-being

How regional versus global thresholds for physical activity and grip strength influence physical frailty prevalence and mortality estimates in PURE: a prospective multinational cohort study of community-dwelling adults

Objectives Handgrip strength and physical activity are commonly used to evaluate physical frailty; however, their distribution varies worldwide. The thresholds that identify frail individuals have been established in high-income countries but not in low-income and middle-income countries. We created two adaptations of physical frailty to study how global versus regional thresholds for handgrip strength and physical activity affect frailty prevalence and its association with mortality in a multinational population.Design, setting and participants Our sample included 137 499 adults aged 35–70 years (median age: 61 years, 60% women) from Population Urban Rural Epidemiology Studies community-dwelling prospective cohort across 25 countries, covering the following geographical regions: China, South Asia, Southeast Asia, Africa, Russia and Central Asia, North America/Europe, Middle East and South America.Primary and secondary outcome measures We measured and compared frailty prevalence and time to all-cause mortality for two adaptations of frailty.Results Overall frailty prevalence was 5.6% using global frailty and 5.8% using regional frailty. Global frailty prevalence ranged from 2.4% (North America/Europe) to 20.1% (Africa), while regional frailty ranged from 4.1% (Russia/Central Asia) to 8.8% (Middle East). The HRs for all-cause mortality (median follow-up of 9 years) were 2.42 (95% CI: 2.25 to 2.60) and 1.91 (95% CI: 1.77 to 2.06) using global frailty and regional frailty, respectively, (adjusted for age, sex, education, smoking status, alcohol consumption and morbidity count). Receiver operating characteristic curves for all-cause mortality were generated for both frailty adaptations. Global frailty yielded an area under the curve of 0.600 (95% CI: 0.594 to 0.606), compared with 0.5933 (95% CI: 0.587 to 5.99) for regional frailty (p=0.0007).Conclusions Global frailty leads to higher regional variations in estimated frailty prevalence and stronger associations with mortality, as compared with regional frailty. However, both frailty adaptations in isolation are limited in their ability to discriminate between those who will die during 9 years’ follow-up from those who do not

‘Malnutrition: A serious concern among hospitalized patients’ a cohort study of nutritional screening among admitted patients using GRAZ malnutrition tool- GMT

Objective: To identify the risks, causes, and degree of malnutrition among admitted patients using GRAZ Malnutrition Screening tool with gender and age groups comparison among private and public hospitals. Materials & Methods: A comparative cohort study was conducted upon 385 admitted patients of two Government and private hospitals from 1st Dec- 2019 to 31st March- 2020. A standardized validated tool was used with categories of weight loss within the last 3 months, BMI, changes in appetite, the severity of the disease, and age greater than 65 with a cut-off score of 3. The data was entered and analyzed through SPSS- Version 19 by computing, frequency, percentages, and Chi-Square test, with significant cut-off limit for P-Value was set at 0.05. Results: Among the 385 admitted patients 52.2 % (n= 201) were males and 48 % (n=184) females. The vulnerable age group was 39-58 Year with 40 % (n= 157) while 33.5 % (n= 129) were among 28-38 Year. Only 6 % (n= 21) were under-weight with BMI <18 / < 20. The risk of malnutrition among admitted females was 65.7 % (n= 121) as compared to 52.2 % males (n= 105) with GMS >3.&nbsp

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc