Working with Wolt

In this paper, we present the results of an ethnographic study focusing on food deliveries for the digital platform Wolt. The platform manages food transport ordered by customers to be delivered at home from restaurants, and subcontracts the transport to workers called 'couriers', who act as independent firms or entrepreneurs. The paper is based on six months of participant observation, during which time the first author worked as a courier, as well as on ad-hoc conversations and semi-structured interviews with other couriers. We describe couriers' work for the platform and discuss our findings using Möhlmann and Zalmanson's definition of algorithmic management. We found both similarities and differences. It was noticeable that the couriers were positive about their work that no penalties or wage reductions were enforced, and that human support complemented the platform's algorithmic management. Thus, the algorithmic management we observed is neither harsh (as it has been described on other platforms including Uber), nor like the algorithmic despotism present on Instacart, for example. Hence, we refer to it as 'lenient algorithmic management' and underline the importance of adding new perspectives to our understanding of what algorithmic management can be, as well as looking at the context in which it is practised. To complement this finding of lenient algorithmic management, we present a set of strategies couriers must engage in to be effective on the platform: Thus, couriers must 1) schedule their work for peak hours to limit the amount of time they waste, 2) bundle orders to increase their payment per tour, 3) make use of support to handle customers and cancel orders involving delays, and 4) make use of the ecology of local support structures. The contribution of this paper is to add new perspectives to the way we perceive algorithmic management by presenting a lenient form of algorithmic management and indicating the importance of looking at the context in which it is practised, while describing what it takes to be an effective worker on the Wolt platform.

Metabolic disorders in poultry (review)

The rapid growth of demand for poultry products requires its sufficient production by specialized farms of various forms of ownership. However, such production needs are not always adequate to the selection approach, the incubation component, the basic requirements of veterinary and sanitary and zoohygienic support, breed and age characteristics of keeping and raising poultry. Therefore, one of the crucial components of obtaining biologically complete, high-quality and fast-paying products of the poultry industry, including all stages of its production, is human support of the main links of ontogenesis (development after birth) of the bird. The issue of not only the creation of the genetic potential of the parent bird of different species and areas of productivity, but also the provision of veterinary and sanitary conditions for their maintenance, breeding and breeding remains relevant. However, the current economic conditions have forced the heads of enterprises and veterinary departments to some extent bypass the planned laboratory tests of feed, water and blood, which, although not complete, but informative enough to trace the main periods of growth and development of the bird. Slight deterioration of the mode and quality of feeding, changes in the parameters of the microclimate are reflected in changes in blood parameters. And what about the spoilage of feed, water, violation of veterinary and sanitary maintenance of poultry: the lack of preventive treatments with vitamin-mineral, hepatoprotective and enzyme preparations, pre- and probiotics. Which can lead to metabolic disorders in poultry. Which can occur due to disorders of protein, lipid, carbohydrate, vitamin, macro- and micromineral metabolism. As a result, there are significantly popular diseases such as: uric acid diathesis, cannibalism, osteoporosis and osteomalacia, perosis, rickets, obesity, E-hypovitaminosis. Further reducing productivity, which leads to large economic losses on farms.

Help your communities grow!

We all have been newcomers one day, and gradually we managed to find our way to become experienced users. But how many promising newcomers have abandoned this same journey because they didn't understand our tools or our policies, or lacked human-to-human support? The Wikimedia Foundation Growth team is working on a set of tools that help communities to welcome newcomers and grow wikis in size and quality. These features have been shown to increase the activation, retention, and edit volume of newcomers. Newcomer homepage: a special page that hosts the "Newcomer tasks" and is a good place for a newcomer to get started. The homepage gives access to many resources, including a link to a volunteer mentor who would reply to their questions. Newcomer tasks: a feed of suggested edits that help newcomers learn how to make simple edits on their preferred subjects. Newcomers have been making productive edits through this feed! The feed is located on the homepage, as the starring feature. Help panel: a platform to provide resources to newcomers while they are editing. When newcomers work on "Newcomer tasks", the help panel guides them on what to do. All of these features are available right now, on both desktop and mobile: communities can request their deployment if they want to try them. This presentation will be about discovering the Growth tools, know their benefits, and see how to implement them in order to increase chances to grow your community in a qualitative way. We will also show the future of these features.

Meta-Analysis of Parent Training Programs Utilizing Behavior Intervention Technologies

Behavioral Parent Training (BPT) traditionally occurs in face-to-face (FTF BPT). Recently, Behavioral Intervention Technology (BIT) has been developed to deliver BPT in lieu of or as an adjunct to FTF BPT using websites, computer software, smartphone applications, podcasts, pre-recorded sessions, and teletherapy. The present meta-analysis reviews BIT BPT randomized control and comparison studies to determine the overall efficacy of BITs, if the level of human support significantly effects BIT BPT treatment outcomes, and which populations BIT BPT are effective for, by analyzing the following study variables: socioeconomic status, race, and clinical population. The analyses indicated that, overall, BIT BPT is an effective treatment (g = 0.62), and did not indicate a significant difference between levels of human support (?2 (3) = 4.94, p = 0.18). Analysis did indicate a significant difference between studies that used waitlist or education control groups, compared to studies that used active treatment controls (?2 (1) = 12.90, p = 0.00). The analyses did not indicate a significant difference between clinical population, low socioeconomic status, and racial minority studies. These findings provide preliminary evidence that BIT BPT is effective for treating child and adolescent externalizing behavior in a variety of populations.

Optimizing and operationalizing engagement with a CBM-I smartphone app: A case series (Preprint)

BACKGROUND Engagement with mental health smartphone apps is an understudied, yet critical, construct to understand in the pursuit of more efficacious mental health apps. OBJECTIVE In this manuscript we examine engagement as a multidimensional construct, as well as strategies to enhance engagement for a novel app HabitWorks. HabitWorks delivers a personalized cognitive bias modification for interpretation bias intervention and was originally tested in people traversing the challenging transition from acute psychiatric care to daily life. METHODS Using a case series we evaluate three domains of engagement- behavioral, cognitive, and affective- for three HabitWorks participants. RESULTS This manuscript highlights various strategies to enhance engagement such as human support, personalization, self-monitoring, and privacy and security measures. Our cases illustrate the heterogeneity of engagement patterns and clinical outcomes. CONCLUSIONS With rich participant-level data we emphasize the necessity of studying engagement as a multifaceted construct, and the complexity of the relationship between overall engagement and psychosocial outcomes. Our thorough idiographic exploration of engagement with HabitWorks provides an example of how to optimize and operationalize engagement for other mHealth apps.

Agent-Supported Peer Collaboration in MOOCs

While massive open online courses (MOOCs) can be effective in scaling education, orchestrating collaborative learning activities for large audiences remains a non-trivial task that introduces a series of practical challenges, such as the lack of adequate human support. Even when collaboration takes place, there is uncertainty whether meaningful interactions will occur among learners. This work presents the architecture of a prototype system called PeerTalk. The system was created to enable instructors to easily incorporate real-time collaborative learning activities into their online courses. Furthermore, PeerTalk employs a conversational agent service that aims to scaffold students’ online collaboration and provide valuable guidance, which can be configured by the course instructor. In order to investigate the user-acceptance of the system, two evaluation studies took place. The first one involved a group of experts, i.e., MOOC instructors who are expected to use such a system in their course, whereas the second study featured 44 postgraduate students. The study findings were encouraging in terms of the system efficiency and usability levels, laying the foundation for a conversational agent service, which can effectively scale the support of the teaching staff and be easily integrated in MOOC platforms, creating further opportunities for valuable social interaction among learners.

Human emotion modeling (HEM): an interface for IoT systems

The use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capable to provide such a combination. In this sense, we aim at providing a novel approach based on a modeling language that can be used even by care-givers or non-experts to model human emotion w.r.t. context for human support services. The proposed modeling approach is based on Domain-Specific Modeling Language (DSML) which helps to integrate different IoT data sources in AAL environment. Consequently, it provides a conceptual support level related to the current emotional states of the observed subject. For the evaluation, we show the evaluation of the well-validated System Usability Score (SUS) to prove that the proposed modeling language achieves high performance in terms of usability and learn-ability metrics. Furthermore, we evaluate the performance at runtime of the model instantiation by measuring the execution time using well-known IoT services.

The Influence of Human Support on the Effectiveness of Digital Mental Health Promotion Interventions for the General Population

Mental wellbeing amongst the general population is languishing—exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. Digital mental health promotion interventions, that improve mental health literacy and encourage adoption of evidence-informed practical strategies are essential. However, attrition and non-adherence are problematic in digital interventions. Human support is often applied as an antidote; yet, there is a paucity of randomized trials that compare different human support conditions amongst general population cohorts. Limited trials generally indicate that human support has little influence on adherence or outcomes in DMHPIs. However, providing participants autonomy to self-select automated support options may enhance motivation and adherence.

The Role of Human Support on Engagement in an Online Depression Prevention Program for Youth

Background: Depression is a significant public health problem for adolescents. The goal of this study was to evaluate the moderating role of human support in an online depression prevention program on both depression outcomes and overall engagement with the intervention. CATCH-IT is an Internet-based depression prevention program that has been shown to reduce symptoms for adolescents who report elevated depression symptom scores, compared to a health education (HE) control group. Participants in the CATCH-IT arm received human support (e.g., motivational interviewing, completed contacts). This study analyzes the moderating role of human support on depressive outcomes and engagement, and examines if engagement predicts depression outcomes. Methods: This secondary analysis consists of a randomized controlled trial for adolescents assigned to the CATCH-IT group. Mixed effects modeling, general linear models, and an exploratory multiple linear regression were used to explore the moderating relationship of human support between intervention and overall engagement. Study variables included depression outcomes (e.g., Center for Epidemiological Studies Depression Scale (CESD)), engagement components (e.g., modules completed, time on the site, and characters typed) and human support (e.g., motivational interviews and completed contacts.) Results: Results showed no significant relationship between contacts, motivational interviews, and depression scores. However, motivational interviews increased engagement with the intervention, such that those who received more motivational interviews completed significantly more modules, spent more time on the site, and typed more characters (p < 0.05). The number of contacts increased engagement with the intervention, and those who received more contacts spent more time on the site and typed more characters (p < 0.05). Exploratory multiple linear regression modeling demonstrated that male, African American/Black, and Hispanic/Latinx users were less engaged compared to other users. Lastly, engagement was not a significant predictor of depression outcomes (p > 0.05). Conclusions: The efficacy of CATCH-IT is not better explained by the degree to which participants received doses of human support from providers during the use of this online intervention. This may reveal the high potential of effective online interventions without the blended integration of human support for adolescents. To increase engagement of adolescents with an online depression prevention program, human support may be more efficient when utilizing MI rather than technical support.