Occupational Therapy and COVID-19: How the Transition to Telehealth Has Affected Outcomes for Children: A Systematic Review

This systematic review aimed to evaluate the evidence for the outcomes of telehealth occupational therapy service delivery versus traditional in-person service delivery for children during the peak of the COVID-19 pandemic. Using three academic databases, a search string including the following keywords, telehealth, occupational, and child, returned 825 articles. Five articles were selected for full-text review after screening and employing the inclusion/exclusion criteria. A quality appraisal was completed, leaving two articles in this systematic review. Researchers found that moderate evidence supports the use of telehealth to disseminate occupational therapy service delivery. Researchers also found substantial evidence that supports the use of telehealth in overall satisfaction with services compared to traditional in-person therapy services. This systematic review confirms that telehealth has improved access to occupational therapy services. Further research needs to be conducted on the efficacy of specific interventions in the telehealth setting. © 2023 Taylor & Francis.

GOAL ADJUSTMENT CAPACITIES DURING COVID-19: CONTEXT-DEPENDENT BENEFITS FOR EMOTIONAL WELL-BEING

Increased constraints and lost opportunities inherent in the COVID-19 pandemic can threaten important life goals and erode emotional well-being. Theories of lifespan development have identified goal adjustment capacities (goal disengagement and goal reengagement) as core self-regulatory resources that can buffer against declines in well-being. However, little is known about the pandemic-related contextual circumstances under which goal adjustment capacities may become more or less beneficial for well-being. Using longitudinal data from a nationally-representative sample of Americans across the adult lifespan (aged 18-80, n=286), we examined the consequences of goal adjustment capacities for emotional well-being under circumstances when individuals reported lower or higher constraints than normal in their lives. Specifically, multilevel models tested whether the influence of between-person differences in (Level 2) goal disengagement and goal reengagement on well-being were moderated by (Level 1) within-person fluctuations in perceived constraints. Analyses controlled for age, sex, education, and income. We observed cross-level Goal Reengagement x Perceived Constraints interactions for depressive symptoms, perceived stress, and positive affect (bs = -.11 to .07, ps < .05), but not negative affect. Results showed that the benefits of goal reengagement for depressive symptoms, perceived stress, and positive affect were pronounced on occasions when participants reported lower (vs. higher) than average perceived constraints in their lives. Findings point to the moderating role of pandemic-related contextual circumstances and suggest that goal reengagement may be most beneficial when individuals have fewer constraints than usual in their lives and may thus able to capitalize on opportunities to pursue new attainable goals.

Optimal shutdown strategies for COVID-19 with economic and mortality costs: British Columbia as a case study.

Decision makers with the responsibility of managing policy for the COVID-19 epidemic have faced difficult choices in balancing the competing claims of saving lives and the high economic cost of shutdowns. In this paper, we formulate a model with both epidemiological and economic content to assist this decision-making process. We consider two ways to handle the balance between economic costs and deaths. First, we use the statistical value of life, which in Canada is about C$7 million, to optimize over a single variable, which is the sum of the economic cost and the value of lives lost. Our second method is to calculate the Pareto optimal front when we look at the two variables-deaths and economic costs. In both cases we find that, for most parameter values, the optimal policy is to adopt an initial shutdown level which reduces the reproduction number of the epidemic to close to 1. This level is then reduced once a vaccination programme is underway. Our model also indicates that an oscillating policy of strict and mild shutdowns is less effective than a policy which maintains a moderate shutdown level.

Analysis and Prediction of Player Population Changes in Digital Games During the COVID-19 Pandemic

The demand for video games increased in large scale during the COVID-19 pandemic as people had to stay at home. In this study we investigate the changes in player population of games during the pandemic using our dataset of 1963 games on Steam to generate insights that would be valuable for the game industry to understand the demand in such crisis. We conduct an empirical analysis to analyse changes in player population size and weekly patterns. Also, we investigate the use of machine learning classification models to predict the games that become popular during the pandemic using information about games as features. Our results indicate a 33% of increase of population during the pandemic and diminishing of weekly player population patterns. Also, we identify that the Random Forest model performs better than other classification models in predicting popular games, however, with only a 63% accuracy and tags assigned to games are the most important feature for prediction generation. Our tag analysis reveals Multiplayer, Adventure, Racing and Boardgames are popular during the pandemic. © 2020, Springer Nature Switzerland AG.