The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Transferring clinical prediction models across hospitals and electronic health record systems

Recent years have seen a surge in studies developing clinical prediction models based on electronic health records (EHRs) as a result of advances in machine learning techniques and data availability. Yet, validation and implementation of such models in practice are rare, in part because EHR-based clinical prediction models are more difficult to apply to new data sets than results of classical clinical studies due to less controlled clinical environments. In this paper we propose to use the theoretical framework of domain adaptation to analyze the problem of transferring machine-learning-based clinical prediction models across different hospitals and EHR systems. Using the model of Thoral et al. [12] predicting patient-level risk of readmission and mortality after intensive care unit discharge as a case study, we discuss, apply and compare multiple domain adaptation methods. We transfer the model from the original source data set to two new target data sets. We find that, while model performance deteriorates substantially when applying a model developed for one data set to another directly, updating models with training data from the target set and using methods that explicitly model differences in data sets always improves model performance. In a simulation experiment, we show that having access to data or model parameters from another hospital can substantially reduce the amount of data required to build an accurate prediction model for a new hospital. We also show that these performance gains diminish with increasing availability of data from the target hospital

Near-Infrared Fluorescence Tumor-Targeted Imaging in Lung Cancer: A Systematic Review

Lung cancer is the most common cancer type worldwide, with non-small cell lung cancer (NSCLC) being the most common subtype. Non-disseminated NSCLC is mainly treated with surgical resection. The intraoperative detection of lung cancer can be challenging, since small and deeply located pulmonary nodules can be invisible under white light. Due to the increasing use of minimally invasive surgical techniques, tactile information is often reduced. Therefore, several intraoperative imaging techniques have been tested to localize pulmonary nodules, of which near-infrared (NIR) fluorescence is an emerging modality. In this systematic review, the available literature on fluorescence imaging of lung cancers is presented, which shows that NIR fluorescence-guided lung surgery has the potential to identify the tumor during surgery, detect additional lesions and prevent tumor-positive resection margins