Skill-driven recommendations for job transition pathways.

Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

Evolving robot empathy towards humans with motor disabilities through artificial pain generation.

In contact assistive robots, a prolonged physical engagement between robots and humans with motor disabilities due to shoulder injuries, for instance, may at times lead humans to experience pain. In this situation, robots will require sophisticated capabilities, such as the ability to recognize human pain in advance and generate counter-responses as follow up emphatic action. Hence, it is important for robots to acquire an appropriate pain concept that allows them to develop these capabilities. This paper conceptualizes empathy generation through the realization of synthetic pain classes integrated into a robot's self-awareness framework, and the implementation of fault detection on the robot body serves as a primary source of pain activation. Projection of human shoulder motion into the robot arm motion acts as a fusion process, which is used as a medium to gather information for analyses then to generate corresponding synthetic pain and emphatic responses. An experiment is designed to mirror a human peer's shoulder motion into an observer robot. The results demonstrate that the fusion takes place accurately whenever unified internal states are achieved, allowing accurate classification of synthetic pain categories and generation of empathy responses in a timely fashion. Future works will consider a pain activation mechanism development.

Assessing Implicit Cognition Among Patients Lost to Follow-up for HIV Care: A Preliminary Study.

OBJECTIVE: While a growing body of research indicates that implicit cognitive processes play an important role in a range of health behaviors, the assessment of these impulsive, associative mental processes among patients living with HIV has received little attention. This preliminary study explored how multidimensional scaling (MDS) could be used to assess implicit cognitive processes among patients lost to follow-up for HIV care and develop interventions to improve their engagement. METHOD: The sample consisted of 33 patients who were identified as lost to follow up for HIV care at two urban hospitals. Participants were randomly assigned to either the MDS assessment program or control group. All participants underwent measures designed to gauge behavioral change intentions and treatment motivation. Assessment group participants were interviewed to determine their reactions to the assessment program. RESULTS: The MDS assessment program identified cognitive processes and their relationship to treatment-related behaviors among assessment group participants. Assessment group participants reported significantly greater behavior change intentions than those in the control group (p =.02; Cohen's d = 0.84). CONCLUSION: MDS shows promise as a tool to identify implicit cognitive processes related to treatment-related behaviors. Assessments based on MDS could serve as the basis for patient-centered clinical interventions designed to improve treatment adherence and HIV care engagement in general.

Pandemic preparedness - Risk management and infection control for all respiratory infection outbreaks.

BACKGROUND: There has been substantial effort and activity in regards to pandemic planning, preparedness and response, mainly in the realm of public health. However, general practitioners and other primary care providers are important players in the health response to a pandemic. OBJECTIVE: To discuss the importance of general practice preparedness for managing respiratory infection outbreaks and to provide a model for the general practice response. DISCUSSION: Pandemic planning and preparedness in general practice is ultimately a crucial risk management exercise, the cornerstone of which is sound infection control. As planning will be significantly aided by, and should extend to, other respiratory outbreaks, we propose a framework for managing outbreaks of respiratory infections with a focus on planned, practised and habitual infection control measures, and a stepwise response according to the extent and severity of the outbreak.