The flexibility allowed by the mobilization of technology disintegrated the traditional work-life boundary for most professionals. Whether working from home is the key or impediment to academics' ...efficiency and work-life balance became a daunting question for both scientists and their employers. The recent pandemic brought into focus the merits and challenges of working from home on a level of personal experience. Using a convenient sampling, we surveyed 704 academics while working from home and found that the pandemic lockdown decreased the work efficiency for almost half of the researchers but around a quarter of them were more efficient during this time compared to the time before. Based on the gathered personal experience, 70% of the researchers think that in the future they would be similarly or more efficient than before if they could spend more of their work-time at home. They indicated that in the office they are better at sharing thoughts with colleagues, keeping in touch with their team, and collecting data, whereas at home they are better at working on their manuscript, reading the literature, and analyzing their data. Taking well-being also into account, 66% of them would find it ideal to work more from home in the future than they did before the lockdown. These results draw attention to how working from home is becoming a major element of researchers' life and that we have to learn more about its influencer factors and coping tactics in order to optimize its arrangements.
Abstract
Background
The amount and value of researchers’ peer review work is critical for academia and journal publishing. However, this labor is under-recognized, its magnitude is unknown, and ...alternative ways of organizing peer review labor are rarely considered.
Methods
Using publicly available data, we provide an estimate of researchers’ time and the salary-based contribution to the journal peer review system.
Results
We found that the total time reviewers globally worked on peer reviews was over 100 million hours in 2020, equivalent to over 15 thousand years. The estimated monetary value of the time US-based reviewers spent on reviews was over 1.5 billion USD in 2020. For China-based reviewers, the estimate is over 600 million USD, and for UK-based, close to 400 million USD.
Conclusions
By design, our results are very likely to be under-estimates as they reflect only a portion of the total number of journals worldwide. The numbers highlight the enormous amount of work and time that researchers provide to the publication system, and the importance of considering alternative ways of structuring, and paying for, peer review. We foster this process by discussing some alternative models that aim to boost the benefits of peer review, thus improving its cost-benefit ratio.
Knowing who to target with certain messages is the prerequisite of efficient public health campaigns during pandemics. Using the COVID-19 pandemic situation, we explored which facets of the ...society-defined by age, gender, income, and education levels-are the most likely to visit social gatherings and aggravate the spread of a disease. Analyzing the reported behavior of 87,169 individuals from 41 countries, we found that in the majority of the countries, the proportion of social gathering-goers was higher in male than female, younger than older, lower-educated than higher educated, and low-income than high-income subgroups of the populations. However, the data showed noteworthy heterogeneity between the countries warranting against generalizing from one country to another. The analysis also revealed that relative to other demographic factors, income was the strongest predictor of avoidance of social gatherings followed by age, education, and gender. Although the observed strength of these associations was relatively small, we argue that incorporating demographic-based segmentation into public health campaigns can increase the efficiency of campaigns with an important caveat: the exploration of these associations needs to be done on a country level before using the information to target populations in behavior change interventions.
Quantifying evidence is an inherent aim of empirical science, yet the customary statistical methods in psychology do not communicate the degree to which the collected data serve as evidence for the ...tested hypothesis. In order to estimate the distribution of the strength of evidence that individual significant results offer in psychology, we calculated Bayes factors (BF) for 287,424 findings of 35,515 articles published in 293 psychological journals between 1985 and 2016. Overall, 55% of all analyzed results were found to provide BF > 10 (often labeled as strong evidence) for the alternative hypothesis, while more than half of the remaining results do not pass the level of BF = 3 (labeled as anecdotal evidence). The results estimate that at least 82% of all published psychological articles contain one or more significant results that do not provide BF > 10 for the hypothesis. We conclude that due to the threshold of acceptance having been set too low for psychological findings, a substantial proportion of the published results have weak evidential support.
The importance of context in behavioral interventions is undeniable, yet few intervention studies begin with a systematic investigation of the contextual factors that influence the behavior in ...question. This is largely due to the lack of a reliable method for doing so. In recognition of this gap in the field, we have developed a procedure called the Choice Context Exploration that uses machine learning tools to examine the contextual factors that influence a targeted behavior. We demonstrate the steps of Choice Context Exploration using the example of the behavioral choice between using stairs or an elevator. Potential contextual factors were identified by laypeople and experts, and two surveys were created to measure both the behavior and choice, as well as the beliefs of participants. We estimated the effect of contextual factors on participants’ behavior and were able to identify the most influential ones in relation to the studied choice. We achieved an accurate prediction of whether participants would choose the stairs or the elevator based on contextual information in 91.43% of cases on previously unseen data. We also found that participants had different beliefs about what influenced their choice in this situation and that they could be divided into different groups based on these beliefs. Our results suggest that the Choice Context Exploration is a useful procedure for collecting and assessing contextual factors in a given choice setting, which can aid in the planning of behavioral interventions by significantly reducing the number of potential interventions that are likely to be effective.
Plain Language Summary
Examining Contextual Factors in Behavioral Choices
This study aimed to address the lack of systematic investigation into contextual factors influencing targeted behaviors in interventions. The researchers developed a procedure called Choice Context Exploration, which employs machine learning tools to examine such factors. Using the example of choosing between stairs and an elevator, potential contextual factors were identified, and surveys were administered to measure behavior, choice, and beliefs. The analysis revealed influential contextual factors and achieved a 91.43% accurate prediction of participants’ choices based on unseen data. Participants held different beliefs, and can be grouped by these beliefs. The study concludes that Choice Context Exploration is valuable for collecting and assessing contextual factors, aiding in behavioral intervention planning by reducing potential interventions. However, limitations include a narrow focus on one behavior and the need for further investigation into generalizability.
Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to ...do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country’s sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.
Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to ...do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.
The current research investigates whether higher economic inequality disproportionately intensifies the financial hardship of low-income individuals. We propose that higher economic inequality ...increases financial hardship for low-income individuals by reducing their ability to rely on their community as a buffer against financial difficulties. This may occur, in part, because a frayed community buffer reduces low-income individuals' propensity to seek informal financial support from others. We provide empirical support across eight studies (sample size N = 1,029,900) from the United States, Australia and rural Uganda, through correlational and experimental data, as well as an instrumental variable analysis. On average across our studies, a 1 s.d. increase in economic inequality is associated with an increase of financial hardship among low-income individuals of 0.10 s.d. We discuss the implications of these results for policies aimed to help people living in poverty buffer against the adverse effects higher economic inequality imposes on them.
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