Altruism in psychotherapy: altruistic acts as an adjunct to psychotherapy

This study will explore the impact of altruism on the mind, brain, and body in order to investigate the potential value of using guided altruistic behavior as an adjunct to attaining the goals of psychotherapy. there is considerable evidence from religious and social practices across cultures, groups like Alcoholics Anonymous, and epidemiological research that point to the positive physical and psychological effects of helping others. This idea was initially stimulated by findings that demonstrated that altruism stimulates brain regions also important to the processes and goals of successful psychotherapy. It is hypothesized that engaging in altruistic behaviors will stimulate emotions, thoughts, and neurobiological processes that will enhance the therapeutic process from a biopsychosocial model. The relevant literature suggests that there may be a correlation between altruism and achieving the goals of therapy

Examining Racial Disparities in Criminal Case Outcomes among Indigent Defendants in San Francisco

We reviewed 10,753 complete case records, consisting of cases between 2011 and 2014, from the San Francisco Public Defender's Office. These data were stored in the Public Defender's GIDEON case management system, which draws from data maintained by the San Francisco County Superior Court's larger case management system database. Unlike previous studies that rely solely on arrest and conviction data, these records cover the entire pretrial process, providing a richer portrait of the experiences of defendants in the criminal justice system. These data can help policymakers and stakeholders understand whether racial disparities exist in the outcomes of San Francisco criminal cases, including cases resolved by plea bargains , and how bargaining affects disparities in other areas of the criminal justice system, such as corrections. Where disparities were seen, we sought to understand them and to evaluate what changes could be made to ensure that similarly situate d individuals receive equal and race - neutral treatment in the criminal justice system. Such information could assist the Public Defender, the San Francisco District Attorney, the San Francisco Police Department, and other criminal justice stakeholders to ensure equitable treatment of all San Franciscans

Postsecondary Education Students with Disabilities’ Perceptions of Occupational Therapy-Led Coaching

Background: Students with disabilities attending postsecondary education (PSE) institutions have poor degree progression, retention, and graduation rates. PSE institutions are addressing these challenges in various ways, including the delivery of occupational therapy (OT)-led coaching. There is emerging evidence that coaching increases academic success and self-determination in PSE. The students’ perspectives about the benefits of OT-led coaching intervention has yet to be explored. Method: A phenomenological study was conducted using transcribed semi-structured interviews with 18 college students with disabilities. Qualitative data analysis was conducted through an immersive inter-coder process that included independent coding, comparison of codes, discrepancy resolution to combine or redefine codes, and theme identification. Results: Overall, the participants reported perceiving the OT-led coaching intervention as beneficial to them. Specifically, four major themes emerged from the data: the personal and academic growth achieved, the benefits of an open and supportive environment in the coaching program, the participants’ perception of self-identified goal achievement, and the importance of accountability and engagement. Conclusion: The students with disabilities perceived that the OT-led coaching intervention was beneficial and identified aspects of the intervention that were most useful to them, including the emotional and material support

Patient Perspectives of the Doctor-at-Home Service

Introduction. Home health care has been established as an effective model for reducing mortality in the elderly. The Doctor-at-Home Service at the Community Health Centers of Burlington (CHCB) has offered home health care to Burlington residents since January 2015. Dr. Karen Sokol, MD, alone provides care to 176 patients at their homes, including at-home palliative care. CHCB hope to expand this program by hiring more providers. Objective. To understand the impact of the Doctor-at-Home Service from the pa- tients’ perspective. Methods. A survey was administered to a cohort of eighteen patients over an 8- week period and addressed topics such as barriers to healthcare, benefits, and costs associated with doctor-at-home programs. A theme analysis on the responses was then conducted to reflect patient opinions. Available summary data describing the pa- tient population was also analyzed. Results. The Doctor- at- Home program serves patients ranging from 26 to 100 years old, with the majority of the patient population comprised of senior citizens. Prior to at home care, patients faced barriers such as lack of transportation, negative past experi- ences, anxiety, and distance from relatives. Four main themes from patient responses were physician-patient relationship, convenience, quality of care, and environment of care. Discussion. Evidence is compelling that there is a desire and need for an exten- sion of the Doctor-at-Home program to reach additional patients. Doctor-at-Home pro- grams could eliminate identified barriers and provide quality care to patients, especially those with specific barriers to access.https://scholarworks.uvm.edu/comphp_gallery/1256/thumbnail.jp

Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective

Many research domains use data elicited from "citizen scientists" when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants' abilities. The model is described in the context of an ecological application that involves crowdsourced classifications of georeferenced coral-reef images from the Great Barrier Reef, Australia. The latent variable of interest is the proportion of coral cover, which is a common indicator of coral reef health. The participants' abilities are expressed in terms of sensitivity and specificity of a correctly classified set of points on the images. The model also incorporates a spatial component, which allows prediction of the latent variable in locations that have not been surveyed. We show that the model outperforms traditional weighted-regression approaches used to account for uncertainty in citizen science data. Our approach produces more accurate regression coefficients and provides a better characterization of the latent process of interest. This new method is implemented in the probabilistic programming language Stan and can be applied to a wide number of problems that rely on uncertain citizen science data.Comment: 18 figures, 5 table

Bayesian spatio-temporal models for stream networks

Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. In this paper, we introduce a new family of dynamic spatio-temporal models, in which spatial dependence is established based on stream distance and temporal autocorrelation is incorporated using vector autoregression approaches. We propose several variations of these novel models using a Bayesian framework. Our results show that our proposed models perform well using spatio-temporal data collected from real stream networks, particularly in terms of out-of-sample RMSPE. This is illustrated considering a case study of water temperature data in the northwestern United States.Comment: 26 pages, 10 fig

Increasing trust in new data sources: crowdsourcing image classification for ecology

Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data.Comment: 25 pages, 10 figure