Counterfactual Theory as an Under-Utilised Analytical Framework for Studying Precarious Work Experiences

This is the author accepted manuscript. The final version is available from Emerald via the DOI in this record.Purpose – The present article seeks to explore experiences and attitudes associated with ‘precarious work’, an umbrella term for insecure, casual, flexible, contingency, non-standard and zero-hour types of employment. Design/methodology/approach – The investigation was carried-out through two studies. The “outside-in” view was represented by business undergraduates (n=56), responding to a four-item questionnaire on precarious work. It was contrasted with the “inside-out” perspective of migrant, care and hospitality workers (n=72) expressed in 48 in-depth interviews, and four focus groups. Findings – Participant narratives included counterfactual comparisons that were more often of a downward (“it could have been worse”) than of an upward (“not as good as it could have been”) kind. Precarious participants spontaneously remarked that they were “lucky” (rather than “unlucky”) to be in precarious work. Implications – Precarious work is likely to give rise to insecurity, uncertainty and vulnerability. However, this study distinguishes between the perspectives of “outside-in” observers, and “insideout” participants. The former view was aligned with the standard view of work social scientists, yet the latter ran counter to both. Interestingly, the narratives of participants were compatible with the self-evaluations of people exposed to other hardships (like natural disasters). Originality/value – There is a limited research on how the use of counterfactual thinking and difference of vantage points shapes attitudes and evaluations of precariousness. To our knowledge, this is the first study which has identified and explained the unprompted use of ‘luck’ in the narratives of precarious workers

Medicine in words and numbers: a cross-sectional survey comparing probability assessment scales

Contains fulltext : 56355.pdf ( ) (Open Access)Background / In the complex domain of medical decision making, reasoning under uncertainty can benefit from supporting tools. Automated decision support tools often build upon mathematical models, such as Bayesian networks. These networks require probabilities which often have to be assessed by experts in the domain of application. Probability response scales can be used to support the assessment process. We compare assessments obtained with different types of response scale. Methods / General practitioners (GPs) gave assessments on and preferences for three different probability response scales: a numerical scale, a scale with only verbal labels, and a combined verbal-numerical scale we had designed ourselves. Standard analyses of variance were performed. Results / No differences in assessments over the three response scales were found. Preferences for type of scale differed: the less experienced GPs preferred the verbal scale, the most experienced preferred the numerical scale, with the groups in between having a preference for the combined verbal-numerical scale. Conclusion / We conclude that all three response scales are equally suitable for supporting probability assessment. The combined verbal-numerical scale is a good choice for aiding the process, since it offers numerical labels to those who prefer numbers and verbal labels to those who prefer words, and accommodates both more and less experienced professionals.8 p

Standards and Practices for Forecasting

One hundred and thirty-nine principles are used to summarize knowledge about forecasting. They cover formulating a problem, obtaining information about it, selecting and applying methods, evaluating methods, and using forecasts. Each principle is described along with its purpose, the conditions under which it is relevant, and the strength and sources of evidence. A checklist of principles is provided to assist in auditing the forecasting process. An audit can help one to find ways to improve the forecasting process and to avoid legal liability for poor forecasting

The Shackles of Practice: History of psychology, research assessment and the curriculum

The history of psychology is being increasingly marginalized in British universities. In this article we argue that this marginalization has been brought about by a combination of material circumstances resulting from the marketization of the UK Higher Education sector. One consequence of this, the statutory audit known as the Research Excellence Framework, has made it increasingly difficult to undertake historical work as it has traditionally been done in UK Psychology Departments. At best such a situation challenges the ambition for historical work to have an impact on psychology. At worst it potentially renders the history of psychology irrelevant. Yet the theoretical justification for history of psychology has never been stronger. Psychology’s subject matter is neither exclusively natural nor entirely socially constructed, but lies on that “somewhat suspect borderland between physiology and philosophy” as Wilhelm Wundt put it. The discipline’s ontological claims are therefore always made from within epistemological frameworks which are themselves products of particular historical contexts. Such arguments have persuaded us that history of psychology has a fundamental role to play within the wider discipline. Yet as historians we cannot ignore the constraining social and material circumstances in which our field operates. We conclude that although the constraints of practice suggest that its prospects for influencing its parent discipline are seriously challenged, there are nevertheless opportunities for the history of psychology areas such as the undergraduate curriculum

Cross Adaptation - Heat and Cold Adaptation to Improve Physiological and Cellular Responses to Hypoxia

To prepare for extremes of heat, cold or low partial pressures of O2, humans can undertake a period of acclimation or acclimatization to induce environment specific adaptations e.g. heat acclimation (HA), cold acclimation (CA), or altitude training. Whilst these strategies are effective, they are not always feasible, due to logistical impracticalities. Cross adaptation is a term used to describe the phenomenon whereby alternative environmental interventions e.g. HA, or CA, may be a beneficial alternative to altitude interventions, providing physiological stress and inducing adaptations observable at altitude. HA can attenuate physiological strain at rest and during moderate intensity exercise at altitude via adaptations allied to improved oxygen delivery to metabolically active tissue, likely following increases in plasma volume and reductions in body temperature. CA appears to improve physiological responses to altitude by attenuating the autonomic response to altitude. While no cross acclimation-derived exercise performance/capacity data have been measured following CA, post-HA improvements in performance underpinned by aerobic metabolism, and therefore dependent on oxygen delivery at altitude, are likely. At a cellular level, heat shock protein responses to altitude are attenuated by prior HA suggesting that an attenuation of the cellular stress response and therefore a reduced disruption to homeostasis at altitude has occurred. This process is known as cross tolerance. The effects of CA on markers of cross tolerance is an area requiring further investigation. Because much of the evidence relating to cross adaptation to altitude has examined the benefits at moderate to high altitudes, future research examining responses at lower altitudes should be conducted given that these environments are more frequently visited by athletes and workers. Mechanistic work to identify the specific physiological and cellular pathways responsible for cross adaptation between heat and altitude, and between cold and altitude, is warranted, as is exploration of benefits across different populations and physical activity profiles

Cognitive workload monitoring in virtual reality based rescue missions with drones

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic outcomes. Therefore, cognitive workload monitoring is the key to prevent the rescuers from taking dangerous decisions. Due to the difficulties of gathering data during real SAR missions, we rely on virtual reality. In this work, we use a simulator to induce three levels of cognitive workload related to SAR missions with drones. To detect cognitive workload, we extract features from different physiological signals, such as electrocardiogram, respiration, skin temperature, and photoplethysmography. We propose a recursive feature elimination method that combines the use of both an eXtreme Gradient Boosting (XGBoost) algorithm and the SHapley Additive exPlanations (SHAP) score to select the more representative features. Moreover, we address both a binary and a three-class detection approaches. To this aim, we investigate the use of different machine-learning algorithms, such as XGBoost, random forest, decision tree, k-nearest neighbors, logistic regression, linear discriminant analysis, gaussian naïve bayes, and support vector machine. Our results show that on an unseen test set extracted from 24 volunteers, an XGBoost with 24 features for discrimination reaches an accuracy of 80.2% and 62.9% in order to detect two and three levels of cognitive workload, respectively. Finally, our results are open the doors to a fine grained cognitive workload detection in the field of SAR missions