The Influence of Human Connections and Collaboration on Research Grant Success at Various Career Stages: Regression Analysis.

BACKGROUND: Documenting the grant acquisition characteristics of a highly selective group of researchers could provide insights into the research and faculty development of talented individuals, and the insights gained to foster such researchers will help university management strengthen their research capacity. OBJECTIVE: This study examines the role of human connections in the success of biomedical researchers in Japanese universities. METHODS: This study used grant data from the Grants-in-Aid for Scientific Research (GIA) program, the largest competitive research funding program in Japan, to collect information on projects and their implementation systems obtained throughout the participants' careers. Grant success was measured by the number and amounts of the awards obtained while participants occupied the role of principal investigator. Human connections were quantified by the number of projects in which the participants took part as members and were classified by their relationship with the project leader. Data were matched with information on career history, publication performance, and experience of the participants with government-funded programs apart from GIA and were analyzed using univariate and multivariate regression analyses. RESULTS: Early-career interpersonal relationships, as measured using the h-index value of the researchers who provided the participants with their initial experience as project members, had a positive effect on grant success. The experience of contributing to prestigious research programs led by top researchers dramatically increased the cumulative amount of GIA awards received by the participants over time. Univariate logistic regression analyses revealed that more interactions with upper-level researchers resulted in fewer acquisitions of large programs (odds ratio [OR] 0.67, 95% CI 0.50-0.89). Collaboration with peers increased the success rate of ≥2 research grants in large programs in situations in which both the participant and project leader were professors (OR 1.16, 95% CI 1.06-1.26). Tracking the process of research development, we found that collaboration during the periods of 10 to 14 years and 15 to 19 years after completing a doctorate degree determined the size of the project that the participant would obtain-interactions with peer researchers and subordinates during the 10- to 14-year postdegree period had positive effects on ≥2 large-program acquisitions (OR 1.51, 95% CI 1.09-2.09 and OR 1.31, 95% CI 1.10-1.57, respectively), whereas interactions with subordinates during the 15- to 19-year postdegree period also had positive effects (OR 1.25, 95% CI 1.25-1.07). Furthermore, relationships that remained narrowly focused resulted in limited grant success for small programs. CONCLUSIONS: Human networking is important for improving an individual's ability to obtain external funding. The results emphasize the importance of having a high-h-indexed collaborator to obtain quality information early in one's career; working with diverse, nonsupervisory personnel at the midcareer stage; and engaging in synergistic collaborations upon establishing a research area in which one can take more initiatives.

An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences.

Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.

Creating Community During COVID-19: A Virtual Art Gallery to Address Social Isolation During a Pandemic.

The COVID-19 pandemic, a public health crisis, significantly impacted millions of people around the world. "Creating Community During COVID-19" is a community-engaged virtual art gallery that explores resilience, social cohesion, and creativity during the onset of the pandemic in the United States. It aimed to address social isolation and encourage inclusion at a large public university in the early days of the pandemic. The community was invited to submit artworks that reflected how they are staying connected during the pandemic. The artworks were then qualitatively analyzed and highlighted three key themes: (1) reflecting (turning inward), (2) advocating (turning outward), and (3) engaging (coming together). This arts-based project demonstrates promise as a creative approach for promoting social cohesion and positive health and well-being, especially in times of uncertainty.

Infection prevention and control across the continuum of COVID-19 care: A qualitative study of patients', caregivers' and providers' experiences.

INTRODUCTION: Healthcare facilities adopted restrictive visitor policies as a result of the COVID-19 (COVID) pandemic. Though these measures were necessary to promote the safety of patients, families and healthcare providers, it led to isolation and loneliness amongst acute care inpatients that can undermine patient rehabilitation and recovery. The study objectives were to (1) explore how infection prevention and control (IP&C) measures impacted stakeholders' perceptions of care quality and interactions with others and (2) investigate how these experiences and perceptions varied across stakeholder groups and care settings. METHODS: A qualitative descriptive study was conducted. Patients and their families from an inpatient COVID rehabilitation hospital and healthcare providers from an acute or rehabilitation COVID hospital were interviewed between August 2020 and February 2021. RESULTS: A total of 10 patients, 5 family members and 12 healthcare providers were interviewed. Four major themes were identified: (1) IP&C measures challenged the psychosocial health of all stakeholders across care settings; (2): IP&C measures precipitated a need for greater relational care from HCPs; (3) infection prevention tenets perpetuated COVID-related stigma that stakeholders experienced across care settings; and (4) technology was used to facilitate human connection when IP&C limited physical presence. CONCLUSION: IP&C measures challenged psychosocial health and maintenance of vital human connections. Loneliness and isolation were felt by all stakeholders due to physical distancing and COVID-related stigma. Some isolation was mitigated by the relational care provided by HCPs and technological innovations used. The findings of the study underscore the need to balance safety with psychosocial well-being across care settings and beyond the patient-provider dyad. PATIENT AND PUBLIC CONTRIBUTION: This study was informed by the Patient-Oriented Research Agenda and developed through consultations with patients and family caregivers to identify priority areas for rehabilitation research. Priority areas identified that informed the current study were (1) the need to focus on the psychosocial aspects of recovery from illness and injury and (2) the importance of exploring patients' recovery experiences and needs across the continuum of care. The study protocol, ethics submission, analysis and manuscript preparation were all informed by healthcare providers with lived experience of working in COVID care settings.

Positive transformation towards the Fourth Industrial Revolution: empirical evidence from a technology company.

This article explores the question of how organizations can transform constructively and positively towards the Fourth Industrial Revolution (4IR). It presents insights into the state of the art on 4IR, positive psychology movements PP1.0 and PP2.0 and particularly on German organizations in the 4IR within the South African context. The study uses a qualitative research approach and presents findings from a study conducted with 16 managers across top, middle and lower management levels in a German engineering organization, based in South Africa, operating in Southern Africa. Findings, discussion, conclusions and recommendations provide insights into emerging themes on the 4IR from perspectives that take the context of discourses on the 4IR in developed and developing countries into account. Findings show the importance of five main themes when transforming into the 4IR: (1) Employee management; (2) Innovative technological and systemic change; (3) Work organization; (4) Environment and (5) Network and cooperation. Human communication and connectivity and a balanced human-machine interaction seem to build the core framework for constructive socio-technological change and a meaningful work environment. Thereby, a focus on the positive transformation requires working through the challenges and dark sides of the 4IR as well as a contextual and culture-specific approach to finally create a meaningful, healthy and optimal functioning work environment for the employees.

Identifying Connectional Silence in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method.

Background: Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human speech offer exceptional opportunity to complement human coding (HC) methods for measurement in large scale studies. Objectives: To test the reliability, efficiency, and sensitivity of a tandem ML-HC method for identifying one feature of clinical importance in serious illness conversations: Connectional Silence. Design: This was a cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations from the Palliative Care Communication Research Initiative multisite cohort study. Setting/Subjects: Hospitalized people with advanced cancer. Measurements: We created 1000 brief audio "clips" of randomly selected moments predicted by a screening ML algorithm to be two-second or longer pauses in conversation. Each clip included 10 seconds of speaking before and 5 seconds after each pause. Two HCs independently evaluated each clip for Connectional Silence as operationalized from conceptual taxonomies of silence in serious illness conversations. HCs also evaluated 100 minutes from 10 additional conversations having unique speakers to identify how frequently the ML screening algorithm missed episodes of Connectional Silence. Results:Connectional Silences were rare (5.5%) among all two-second or longer pauses in palliative care conversations. Tandem ML-HC demonstrated strong reliability (kappa 0.62; 95% confidence interval: 0.47-0.76). HC alone required 61% more time than the Tandem ML-HC method. No Connectional Silences were missed by the ML screening algorithm. Conclusions: Tandem ML-HC methods are reliable, efficient, and sensitive for identifying Connectional Silence in serious illness conversations.