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In response to the Covid-19 crisis, 186 countries implemented direct cash transfers to households, and 181 introduced in-kind programs that lowered the cost of utilities such as electricity, water, transport, and mobile money. During times of crisis, do people prefer in-kind transfers or cash, and why? In this paper, we compare electricity transfers against a benchmark of cash transfers (mobile money) among 2000 rural and urban residents of Kenya with pre-paid electricity meter connections. We offer participants an incentivized choice between electricity transfers or mobile money, totaling approximately USD 10 to 15, and then implement their choice over three months. We generate three main findings. First, participants overwhelmingly prefer cash, with three-quarters of participants opting for mobile money even when offered electricity tokens with a cash value that is 40 percent higher, possibly due to the flexibility in expenditures or credit constraints. Second, despite relatively low baseline electricity consumption, preference for cash is slightly lower in rural areas, possibly due to higher transaction costs for purchasing electricity, lower mobile money penetration, or savings constraints. Third, electricity tokens transfers generate a larger increase in electricity consumption than equivalent cash transfers, suggesting a role for mental accounting; however, we estimate no impact of either electricity or cash transfers on a broad set of socioeconomic outcomes. These patterns suggest that mobile money transfers generate larger welfare gains than electricity credit, at least in settings with high mobile money penetration.

BackgroundThe Internet provides a platform to access health information and support self-management by consumers with chronic health conditions. Despite recognized barriers to accessing Web-based health information, there is a lack of research quantitatively exploring whether consumers report difficulty finding desired health information on the Internet and whether these consumers would like assistance (ie, navigational needs). Understanding navigational needs can provide a basis for interventions guiding consumers to quality Web-based health resources. ObjectiveWe aimed to (1) estimate the proportion of consumers with navigational needs among seekers of Web-based health information with chronic health conditions, (2) describe Web-based health information-seeking behaviors, level of patient activation, and level of eHealth literacy among consumers with navigational needs, and (3) explore variables predicting navigational needs. MethodsA questionnaire was developed based on findings from a qualitative study on Web-based health information-seeking behaviors and navigational needs. This questionnaire also incorporated the eHealth Literacy Scale (eHEALS; a measure of self-perceived eHealth literacy) and PAM-13 (a measure of patient activation). The target population was consumers of Web-based health information with chronic health conditions. We surveyed a sample of 400 Australian adults, with recruitment coordinated by Qualtrics. This sample size was required to estimate the proportion of consumers identified with navigational needs with a precision of 4.9% either side of the true population value, with 95% confidence. A subsample was invited to retake the survey after 2 weeks to assess the test-retest reliability of the eHEALS and PAM-13. ResultsOf 514 individuals who met our eligibility criteria, 400 (77.8%) completed the questionnaire and 43 participants completed the retest. Approximately half (51.3%; 95% CI 46.4-56.2) of the population was identified with navigational needs. Participants with navigational needs appeared to look for more types of health information on the Internet and from a greater variety of information sources compared to participants without navigational needs. However, participants with navigational needs were significantly less likely to have high levels of eHealth literacy (adjusted odds ratio=0.83, 95% CI 0.78-0.89, P

BackgroundThe abundance of health information available online provides consumers with greater access to information pertinent to the management of health conditions. This is particularly important given an increasing drive for consumer-focused health care models globally, especially in the management of chronic health conditions, and in recognition of challenges faced by lay consumers with finding, understanding, and acting on health information sourced online. There is a paucity of literature exploring the navigational needs of consumers with regards to accessing online health information. Further, existing interventions appear to be didactic in nature, and it is unclear whether such interventions appeal to consumers’ needs. ObjectiveOur goal was to explore the navigational needs of consumers with chronic health conditions in finding online health information within the broader context of consumers’ online health information-seeking behaviors. Potential barriers to online navigation were also identified. MethodsSemistructured interviews were conducted with adult consumers who reported using the Internet for health information and had at least one chronic health condition. Participants were recruited from nine metropolitan community pharmacies within Western Australia, as well as through various media channels. Interviews were audio-recorded, transcribed verbatim, and then imported into QSR NVivo 10. Two established approaches to thematic analysis were adopted. First, a data-driven approach was used to minimize potential bias in analysis and improve construct and criterion validity. A theory-driven approach was subsequently used to confirm themes identified by the former approach and to ensure identified themes were relevant to the objectives. Two levels of analysis were conducted for both data-driven and theory-driven approaches: manifest-level analysis, whereby face-value themes were identified, and latent-level analysis, whereby underlying concepts were identified. ResultsWe conducted 17 interviews, with data saturation achieved by the 14th interview. While we identified a broad range of online health information-seeking behaviors, most related to information discussed during consumer-health professional consultations such as looking for information about medication side effects. The barriers we identified included intrinsic barriers, such as limited eHealth literacy, and extrinsic barriers, such as the inconsistency of information between different online sources. The navigational needs of our participants were extrinsic in nature and included health professionals directing consumers to appropriate online resources and better filtering of online health information. Our participants’ online health information-seeking behaviors, reported barriers, and navigational needs were underpinned by the themes of trust, patient activation, and relevance. ConclusionsThis study suggests that existing interventions aimed to assist consumers with navigating online health information may not be what consumers want or perceive they need. eHealth literacy and patient activation appear to be prevalent concepts in the context of consumers’ online health information-seeking behaviors. Furthermore, the role for health professionals in guiding consumers to quality online health information is highlighted.

To ensure a robust pipeline of computer scientists worldwide, male and female students must perceive computer science (CS) as a viable field of study and career. Using expectancy-value theory (EVT) as an interpretive framework, this paper reports on a descriptive case study conducted within a CS course in a private, international high school in South Korea to illuminate factors that influenced student (N=10) enrollment in the class and assessments of CS careers. EVT was chosen as a validated model to understand how students' perceptions and choices influence the value and future participation in an activity (like CS). Data was collected from questionnaires, journal entries, essays, and class discussions over a ten-day unit exploring careers in CS. Data was coded using constructs of expectancy-value theory and informed themes for analysis. Factors that influenced sampled students to enroll in the high school CS class included interests in technology and society, perceived difficulty (challenge), interest in math and science, cultural influences, and wanting early exposure to CS content. Career aspirations in CS illustrated both desirable (i.e., broad applications of CS, positive societal impacts, leveraging creativity) and undesirable (i.e., high ability for CS, stifling of creativity) aspects of CS careers. Gender differences showed that only males (n=7) held negative views of creativity and females (n=3) made connections between CS content and other career fields. The difference shows that attracting more gender diversity means focusing on different interventions for males and females. Half of the students (n=5) reported that learning about CS careers changed prior negative perceptions, suggesting CS career information is important for students to develop accurate value judgments of CS.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

In response to the Covid-19 crisis, 186 countries implemented direct cash transfers to households, and 181 introduced in-kind programs that lowered the cost of utilities such as electricity, water, transport, and mobile money. Do cash or in-kind transfers generate greater welfare improvements? And, does a country’s financial infrastructure affect optimal aid disbursement? Through a parallel set of surveys in two urban regions in Africa—with comparable education, cell phone ownership, and electricity connectivity—we show that optimal government aid disbursement hinges on financial infrastructure. In line with economic theory favoring direct cash transfers, in a randomized experiment in Kenya 95% of urban recipients prefer mobile money over electricity transfers of a similar monetary value. But Kenya is an outlier with high mobile money adoption: this increases its value and reduces transaction costs of buying electricity credit. By contrast, in Ghana—where mobile money is less widespread and the transaction costs for buying electricity are higher—half of recipients prefer electricity transfers, and many are willing to forego significant value to receive electricity instead of mobile money. These results have several important policy implications. First, the optimal government policy in response to an economic crisis is not uniform: cash and in-kind transfers have different advantages that make each suitable for specific contexts. Second, the adoption of modern financial technologies will likely increase the efficiency of government cash transfer programs, even as in-kind transfers continue to be preferred in settings where mobile money uptake is slow. Finally, giving recipients a choice harnesses valuable local information that a policy maker may not have access to.