Who Benefits from Job Creation at County Level? An Analysis of Leakage and Spillover of New Employment Opportunities in Virginia

Using an econometric model system built on county level labor market data, this study allocates new employments in Virginia from 1990 to 2000 into various demographic segments: commuters, residents, and new immigrants. The study finds significant leakage of new employment opportunities in Virginia. 52% of new jobs created in the 1990s in a locality were taken by outside commuters. However, Virginia’s localities also benefit from spillover benefits from job creation elsewhere. Economists need to account for employment leakage and spillover to accurately evaluate the fiscal impacts of potential economic development projects

Housing prices and multiple employment nodes: is the relationship nonmonotonic?

Standard urban economic theory predicts that house prices will decline with distance from the central business district. Empirical results have been equivocal, however. Disjoints between theory and empirics may be due to a nonmonotonic relationship between house prices and access to employment arising from the negative externalities associated with proximity to multiple centres of employment. Based on data from Glasgow (Scotland), we use gravity-based measures of accessibility estimated using a flexible functional form that allows for nonmonotonicity. The results are thoroughly tested using recent advances in spatial econometrics. We find compelling evidence of a nonmonotonic effect in the accessibility measure and discuss the implications for planning and housing policy

Migration and Housing Price Effects of Place-Based College Scholarships

Place-based college scholarships, such as the Kalamazoo Promise, provide students who live in a particular place, and/or who attend a particular school district, with generous college scholarships. An important potential benefit from such “Promise programs” is their short-term effects on local economic development. Generous Promise scholarships provide an incentive for families to locate in a particular place, which may change migration patterns, and potentially boost local employment and housing prices. Using data from the American Community Survey, this paper estimates the average effects of eight relatively generous Promise programs on migration rates and housing prices in their local labor market. The paper finds evidence that Promise programs lead to significantly reduced out-migration rates for at least three years after a Promise program is announced. These reductions in out-migration rates are larger for households with children, and are also larger when we focus on smaller areas around the Promise-eligible zone rather than the entire local labor market. These out-migration effects are large, implying that Promise programs lead to a 1.7% increase in overall population of the local labor market

Can a Smartphone App Make you Feel Super Better? A Pilot Study Utilizing a Multiple Single-Case Design

This dataset contains the de-identified data of four individuals who participated in this pilot study using the mental health app, SuperBetter. A multiple single-case design was used. The dataset contains: 1. Daily distress ratings, known as Subjective Units of Distress (SUDS); 2. Anxiety and depression symptom outcome ratings, captured at four different time points by the Depression Anxiety Stress Scale - 21 Item Version (DASS-21); 3. Life functioning ratings captured at four different time points by the Outcome Questionnaire - 45 Item Version 2nd Edition (OQ-45.2); 4. Demographic information captured by our demographics questionnaire; and 5. Final app rating and appraisal captured by the Mobile Application Rating Scale - User Version (uMARS). The dataset contains both raw data files and graph / figure files for visual analysis. For more information on the background, method, results and discussion of this dataset, see the published research protocol article that covers both this pilot study and main intervention study (Marshall, Dunstan, & Bartik; 2020), and published pilot study article

Apps With Maps - Anxiety and Depression Mobile Apps With Evidence-Based Frameworks: Systematic Search of Major App Stores

Background: Mobile mental health apps have become ubiquitous tools to assist people in managing symptoms of anxiety and depression. However, due to the lack of research and expert input that has accompanied the development of most apps, concerns have been raised by clinicians, researchers, and government authorities about their efficacy.Objective: This review aimed to estimate the proportion of mental health apps offering comprehensive therapeutic treatments for anxiety and/or depression available in the app stores that have been developed using evidence-based frameworks. It also aimed to estimate the proportions of specific frameworks being used in an effort to understand which frameworks are having the most influence on app developers in this area.Methods: A systematic review of the Apple App Store and Google Play store was performed to identify apps offering comprehensive therapeutic interventions that targeted anxiety and/or depression. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was adapted to guide this approach.Results: Of the 293 apps shortlisted as offering a therapeutic treatment for anxiety and/or depression, 162 (55.3%) mentioned an evidence-based framework in their app store descriptions. Of the 293 apps, 88 (30.0%) claimed to use cognitive behavioral therapy techniques, 46 (15.7%) claimed to use mindfulness, 27 (9.2%) claimed to use positive psychology, 10 (3.4%) claimed to use dialectical behavior therapy, 5 (1.7%) claimed to use acceptance and commitment therapy, and 20 (6.8%) claimed to use other techniques. Of the 162 apps that claimed to use a theoretical framework, only 10 (6.2%) had published evidence for their efficacy.Conclusions: The current proportion of apps developed using evidence-based frameworks is unacceptably low, and those without tested frameworks may be ineffective, or worse, pose a risk of harm to users. Future research should establish what other factors work in conjunction with evidence-based frameworks to produce efficacious mental health apps

Effectiveness of Using Mental Health Mobile Apps as Digital Antidepressants for Reducing Anxiety and Depression: Protocol for a Multiple Baseline Across-Individuals Design

Background: The use of mental health mobile apps to treat anxiety and depression is widespread and growing. Several reviews have found that most of these apps do not have published evidence for their effectiveness, and existing research has primarily been undertaken by individuals and institutions that have an association with the app being tested. Another reason for the lack of research is that the execution of the traditional randomized controlled trial is time prohibitive in this profit-driven industry. Consequently, there have been calls for different methodologies to be considered. One such methodology is the single-case design, of which, to the best of our knowledge, no peer-reviewed published example with mental health apps for anxiety and/or depression could be located.Objective: The aim of this study is to examine the effectiveness of 5 apps (Destressify, MoodMission, Smiling Mind, MindShift, and SuperBetter) in reducing symptoms of anxiety and/or depression. These apps were selected because they are publicly available, free to download, and have published evidence of efficacy.Methods: A multiple baseline across-individuals design will be employed. A total of 50 participants will be recruited (10 for each app) who will provide baseline data for 20 days. The sequential introduction of an intervention phase will commence once baseline readings have indicated stability in the measures of participants’ mental health and will proceed for 10 weeks. Postintervention measurements will continue for a further 20 days. Participants will be required to provide daily subjective units of distress (SUDS) ratings via SMS text messages and will complete other measures at 5 different time points, including at 6-month follow-up. SUDS data will be examined via a time series analysis across the experimental phases. Individual analyses of outcome measures will be conducted to detect clinically significant changes in symptoms using the statistical approach proposed by Jacobson and Truax. Participants will rate their app on several domains at the end of the intervention.Results: Participant recruitment commenced in January 2020. The postintervention phase will be completed by June 2020. Data analysis will commence after this. A write-up for publication is expected to be completed after the follow-up phase is finalized in January 2021.Conclusions: If the apps prove to be effective as hypothesized, this will provide collateral evidence of their efficacy. It could also provide the benefits of (1) improved access to mental health services for people in rural areas, lower socioeconomic groups, and children and adolescents and (2) improved capacity to enhance face-to-face therapy through digital homework tasks that can be shared instantly with a therapist. It is also anticipated that this methodology could be used for other mental health apps to bolster the independent evidence base for this mode of treatment.International Registered Report Identifier (IRRID): PRR1-10.2196/1715

Pattern Recognition: Motor Skills Development in Girls Aged 15

The study objective is to determine physical exercises modes when developing motor skills in girls aged 15. Materials and methods. The participants in the study were 40 girls aged 15. To achieve the objectives set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of training tasks; pedagogical experiment, methods of mathematical statistics, methods of mathematical experiment planning, discriminant analysis. To achieve the objective set, the study examined the effect of different variants of performing exercises, specifically: the number of repetitions (x1) and rest intervals (x2) on mastering a press headstand technique. The girls aged 15 were divided into four groups according to the experiment plan. During the learning process, a method of algorithmic orders was used. The student could proceed to the next exercise only after a correct performance of the previous exercise on three consecutive attempts. The number of repetitions needed to teach exercises in each series of training tasks was recorded. The level of proficiency in the physical exercises performance was determined by the alternative method: “performed” or “failed”. A technically correct performance of the exercise gave the students “1” point; a failure to perform the exercise gave them “0” entered in the protocol. Results. The 22 type experiment made it possible to study the multifactorial structure of teaching the girls aged 15, using a program of algorithmic orders; to specify optimal correlations of factors for their use when teaching physical exercises during physical education classes; to collect data for the calculation of regression models of teaching separate series of training tasks and for discriminant analysis to obtain a generalized model of teaching an exercise in general. Conclusions. To select a generalized mode of exercises of the series of tasks when teaching girls aged 15 a press headstand and handstand, the first discriminant function can be used with an emphasis on the most informative variables. The regression equations obtained from the analysis of the 22 full factorial experiment data make it possible to specify and select the most effective modes of exercises for mastering separate series of training tasks

Treating Psychological Trauma in the Midst of COVID-19: The Role of Smartphone Apps

With the COVID-19 pandemic confronting health systems worldwide, medical practitioners are treating a myriad of physical symptoms that have, sadly, killed many thousands of people. There are signs that the public is also experiencing psychological trauma as they attempt to navigate their way through the COVID-19 restrictions impinging on many aspects of society. With unprecedented demand for health professionals' time, people who are unable to access face-to-face assistance are turning to smartphone apps to help them deal with symptoms of trauma. However, the evidence for smartphone apps to treat trauma is limited, and clinicians need to be aware of the limitations and unresolved issues involved in using mental health apps