Open Access Journals at IU Indianapolis
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The Impact of Mental Health First Aid (MHFA) Training in Libraries: A Pilot Study
No full textObjective: This study seeks to explore how Mental Health First Aid (MHFA) training impacts librarians working in both academic health sciences and public libraries. Specifically, it aims to investigate how MHFA training affects their mental health literacy, professional confidence, and their ability to provide mental health information and resources.
Methods: This qualitative research study explores the perspectives and experiences of seven librarians from two different types of libraries, an academic health sciences library and a public library system. Semi-structured interviews were conducted to gather participants\u27 experiences and insights regarding MHFA training. This project received IRB approval.
Results: Mental Health First Aid training enhances participants\u27 awareness of mental health challenges, boosts their professional confidence, and improves mental health literacy. In addition to being a skills-based training that teaches de-escalation techniques, it also helps reduce stigma surrounding mental health conditions. After completing MHFA training, participants report increased confidence in supporting individuals experiencing mental health challenges and assisting them in obtaining appropriate help.
Conclusion: Mental Health First Aid is a valuable workplace training for both academic and public libraries, benefiting all types of library staff. It enhances awareness of mental health conditions and reduces stigma while simultaneously increasing mental health literacy and professional confidence. Additionally, it equips participants with the skills and confidence to appropriately assist individuals, both library staff and users, experiencing mental health challenges. Furthermore, Mental Health First Aid training cultivates greater empathy and compassion, contributing to a more supportive workplace culture that is welcoming and inclusive
Disparities in Timeliness of Care in Anal Cancer: A Retrospective Chart Review
Get PDFBackground and Objective: Approximately 10,540 new cases of anal cancer (AC) will be diagnosed in the USA in 2024, but its incidence has increased two-to-four-fold in the past few decades. Areas with higher social vulnerability have decreased odds of colorectal cancer screening, resulting in delayed healthcare presentation. This project describes the elapsed time between initial symptomatic presentation and diagnosis of AC across 3 possible points of process failure and explores the impact of patient factors and social vulnerability on time to diagnostic resolution.
Project Methods: Patients diagnosed with AC within the Indiana University Health system from 2020-2024 were identified using the electronic health record. Cases were reviewed (n=478); demographics and the following timepoints were noted: initial symptomatic presentation, specialist referral, diagnostic testing, and AC diagnosis. The primary outcome was time between presentation and diagnosis, with diagnostic delay classified as more than six weeks. Patient factors and clinical characteristics were compared to calculate risk ratios to explore associations between these factors and diagnostic delay.
Results: After exclusions, analysis of the cohort (n=193) showed that 99 patients (51.3%) did not experience a delay, and 94 did (48.7%), with a total median SVI of 0.51 (IQR=0.29-0.75) and delay of 39d (IQR=17-90). Patients with a delay (SVI=0.53; IQR=0.30-0.76) had similar SVIs to those without a delay (SVI=0.51; IQR=0.22-0.75; p=0.462). Older patients had a higher risk of experiencing a delay than a younger patient (RR=1.005; CI=1-1.01; p=0.038). Patients working full-/part-time have an increased risk of delay than those retired (RR=0.832; CI=0.721-0.96; p=0.012) or unemployed (RR=0.849; CI=0.746-0.967; p=0.014).
Conclusion and Potential Impact: Older age and employment status are drivers of diagnostic delay after AC presentation. No measures of social vulnerability were associated with delayed care. Further work exploring the process failures to diagnostic resolution will inform interventions targeting barriers and facilitators. As barriers-to-care are reduced, more patients can have improved cancer outcomes and quality of life
Using Implementation Science to Advance Hearing Health Equity in Older Adults
Get PDFBackground/Objectives: Hearing loss is highly prevalent among the 1.2 million older adults who live in Senior Living Communities (SLCs). Under-detection of hearing loss and under-use of hearing devices among residents of SLCs is a significant healthcare disparity, which places older adults at an increased risk for dementia, falls, and depression. Our long-term objective is to improve the hearing health, emotional well-being, and quality-of-life of older adults living in SLCs by implementing hearing healthcare into standard clinical care.
Methods: We focused on the first two of the four interrelated phases of implementation science: exploration and initiation. We partnered with the largest SLC in Indiana and onboarded three of their communities. Each of these communities provide four levels of care, and residents vary across Social Determinants of Health (SDoH), allowing our team to compare hearing health outcomes as a function of residents’ physical/cognitive abilities and SDoH.
Results: As part of the exploration phase, we identified five barriers to hearing healthcare in SLCs: 1) hearing health literacy in the care staff and residents, 2) poor management of hearing devices, 3) poor management of cerumen – ear hygiene – which hinders access to sound, 4) underuse of effective screening tools, and 5) lack of connections to an audiologist and neurotologist. As part of the initiation phase, we collaborated with community stakeholders to create the infrastructure for screening, referring, & implementation of clinical healthcare.
Conclusions/Implications: Implementation science is applying an already known evidence-based practice to a particular problem; in this case, we anticipate that providing effective screening tools, management of cerumen, and access to an audiologist and neurotologist will improve the hearing health, emotional well-being, & quality-of-life in older adults living in SLCs. Our research program will continue to develop, test, and refine our implementation strategies to achieve equity in hearing health outcomes
A Clinically Interpretable Deep Learning Framework for the Detection and Grading of Diabetic Retinopathy
Get PDFObjective: One of the leading causes of blindness in working age adults is diabetic retinopathy (DR) which can result in vision loss if uncontrolled. DR can be detected and graded by fundus retinal imaging, however the amount of images requiring grading creates a burden on ophthalmologists. Thus, a growing demand exists for an optimized DR image reading process. To accomplish this, we propose the use of a deep learning artificial intelligence model to detect and grade DR using lesion feature extraction with clinical interpretation.
Methods: Retinal fundus images were collected from two sources for training (n=608): E-Ophthalmology, and a private Indiana University Eugene & Marilyn Glick Eye Institute dataset. Each dataset was divided into 70% (training), 20% (validation), and 10% (testing) subgroups. An external dataset, the UKBB, was also used (n=944) for evaluation. The AI model assigned images to 5 categories based on lesion features. The model operated through 2 stages: a multi-scale DeepLabV3+ to segment retinal lesions from input fundus images, followed by segmentation predictions for lesion. A classifier incorporates data to predict whether DR is present and the grade for images with DR.
Results: AI performance was analyzed using several metrics to compare against human grading. Developmental results for segmentation were: 0.88 (precision), 0.70 (recall), 0.78 (f1 score), 0.99 (accuracy), 0.94 (AUC), and 0.76 (IOU). Classification of DR results were: 0.89 (precision), 0.6 (recall), 0.78 (f1 score), 0.62 (accuracy), and 0.8 (AUC). External dataset results were: 0.47 (precision), 0.58 (recall), 0.52 (f1 score), 0.58 (accuracy), and 0.65 (AUC).
Conclusions: The proposed deep learning AI framework demonstrates good DR lesion detection and grading performance. Further improvement may allow AI to replace human grading, saving ophthalmologists time and standardizing the grading process. The current approach may be improved by including more lesion features and larger sample sizes
Long Term Effects of Transient Romidepsin Exposure on Osteosarcoma Sarcospheres
Get PDFBackground:Osteosarcoma, the most prevalent primary bone cancer, disproportionally affects adolescents and young adults. Despite aggressive chemotherapeutics, prognosis remains poor, highlighting the need for novel therapeutics. Romidepsin, a histone deacetylase inhibitor, emerged as exceptionally potent and minimally toxic in our screen of 114 FDA-approved oncology drugs using three-dimensional osteosarcoma spheroids (sarcospheres). Due to the genetic complexity of pathogenesis, differences in sensitivity exist across cell lines and patient samples. Understanding the mechanism of romidepsin is crucial for determining predictability of treatment response, especially considering its short half-life and weekly administration regimen.
Methods:Sarcospheres derived from established cell lines (LM7, MG63.3, and 143B) or patient sample (TT2) were exposed to various concentrations of romidepsin for 24 hours. Metabolic activity was measured by resazurin reduction assays at multiple timepoints following romidepsin removal.
Results:Effects of transient romidepsin exposure last at least 14 days. TT2 and LM7 sarcospheres do not increase in size over 48 hours of culture, and recovery is prevented at clinically achievable levels. In contrast, 143B and MG63.3 sarcospheres show growth over 48 hours and romidepsin blocks this growth. For these sarcospheres, concentrations above clinically achievable levels are required to prevent recovery.
Conclusions and Potential Impact:Romidepsin is FDA-approved for weekly administration in lymphoma. Effects caused by transient exposure to romidepsin persisted for 14 days in slow growing patient-derived sarcospheres, suggesting weekly administration is likely sufficient for patients with osteosarcoma. Results of this study are consistent with our previous findings that there are two types of responses to romidepsin. Romidepsin causes a G2 cell cycle block and growth inhibition in 143B and MG63.3 sarcospheres, while it causes DNA damage and cell death in LM7 and TT2 sarcospheres. Further exploration of these mechanistic differences has the potential to personalize medicine by providing better predictability of response to treatment and insight into prognosis
Analyzing the Effectiveness of a Pre-Arrival Time-Out in Pediatric Trauma Resuscitations
No full textBackground: Time-out checklists are commonplace throughout procedural specialties and have been associated with improved care and decreased errors. The WHO has recommended a checklist for trauma care, particularly focused on the trauma resuscitation, to decrease missed injuries and facilitate an efficient resuscitation. However, this checklist does not address pre-arrival preparations that might facilitate a shared mental model for an upcoming resuscitation. The goal of this study was to draft a pre-arrival time-out at Riley Hospital for Children (RHC), emphasizing provider role introductions, patient details, potential care needs upon patient arrival, and patient disposition.
Methods: Pre-arrival preparation for trauma resuscitations were retrospectively reviewed using trauma video review. All resuscitations took place in one of four trauma bays at the RHC Emergency Department. Recordings captured Level 1 trauma activations. The recordings were abstracted for factors which researchers felt would facilitate strong team dynamics and allow for creation of a shared mental model. A REDCap database was created to capture the deidentified data.
Results: Twenty-four resuscitations were available for analysis, time-outs occurred in 29.2% (7/24). Time-out attendance was 13.9 (±2.0) personnel. The average length of time-outs was 59.6 (±19.4) seconds and the average time between the end of the time-out and patient arrival was 4 minutes 53.7 seconds (± 3 minutes 37.0 seconds). Introductions were fully completed in 16.7% (4/24) of recordings and in 57.1% (4/7) of occurrences with a time-out performed. A clear team leader was identified in 29.2% (7/24) of resuscitations. Pre-arrival discussions about the case occurred in 83.3% (20/24) of recordings. A stated need for an item (equipment/medication) occurred in 50.0% (12/24) of recorded pre-hospital discussions.
Conclusion and Potential Impact: The rates of time-outs, introductions, stating patient details, and stating equipment needs before patient arrival were low in many cases. A formalized pre-arrival time-out discussion prior to pediatric trauma resuscitations could be effective in increasing these rates. 
Understanding the COVID-19 Impacts on Social Work Learning Through the Lens of Planned Behavior Theory
Get PDFCOVID-19 was a global pandemic that caused higher education to enter emergency distance learning. Students experienced a myriad of emotional and psychological stressors during this pandemic. While the demand for online learning has been steadily increasing, even in the field of social work, it has nonetheless lagged behind other disciplines due to concerns about limited interpersonal interactions. We attempted to understand students’ intentions, behaviors, and outcomes related to the distance learning method during the COVID-19 pandemic through the lens of Planned Behavior Theory. We used a cross-sectional qualitative study containing some quantitative questions. Study data was drawn from 13 interviews conducted at two Hispanic serving institutions (HSI)s in North Texas. Findings of our study included three major themes: challenges and stressors; attitudes, subjective norms, and perceived behavioral control that increased intentions and adaptive behaviors; and resource gaps that interfered with adaptive behaviors. Exploring students’ perceptions and behavioral responses to learning provides identification of new or hidden pedagogical obstacles and novel approaches to address the challenges in Social Work education. The study findings can contribute to improving resources and addressing gaps in the curriculum as we continue to think critically and creatively about virtual education in the future
Shaping Pedagogical Identities: A Collaborative Autoethnography of a Virtual Doctoral Student Pedagogy Peer Mentoring Group
Get PDFIn this collaborative autoethnography (CAE), we examine a virtual doctoral (PhD) student pedagogy peer mentoring group’s experience within a social work program at a northeastern U.S. public research university. Our focus is on the experiences of PhD students who are preparing to teach. Utilizing CAE as our methodological approach, we identified themes salient to our group experiences. Key themes include navigating role and identity transitions, the value of community, the need for enhanced institutional support, and the role of educators as gatekeepers. Our findings underscore peer mentorship as a crucial component of doctoral training, not only for fostering camaraderie and reducing isolation, but also for enhancing teaching preparedness. We recommend implementing structured peer mentorship, expanding pedagogical training, and strengthening institutional support to better prepare doctoral students for teaching
Implementing Data-Driven Strategies to Improve Course Performance: A Case Study at the University of Houston
Get PDFOne approach the University of Houston (UH) has taken toward reaching its graduation rate goal is implementing strategies to improve course performance. Course grades impact GPA, and the literature consistently shows a strong positive relationship between GPA and likelihood of persistence and degree completion. In 2021, UH launched an initiative to improve student performance in gateway courses through faculty professional development and strategic course redesign. Redesign efforts focused on general education courses with high enrollment, high DWIF rates, and gaps in course DWIF rates across student populations. Redesign elements included more time engaging with course material in a meaningful way, new assignments, and amnesty opportunities. This case study highlights three courses which started with DWIF rates of 41% to 49%. After implementing course redesign strategies unique to each course, DWIF rates decreased by nine to 35 percentage points and performance gaps were improved for students from historically underrepresented groups. Course redesign strategies were analyzed in the context of Ryan and Deci’s (2000) self-determination theory of human motivation. Lessons learned included using disaggregated data to create urgency and accountability, focusing on students’ academic and non-academic needs, and remembering there is no one-size-fits-all method for course redesign
Leveraging Higher Education Institutions for Regional Economic Innovation: The Michigan State University Center for Community and Economic Development Approach
Get PDFInequities continue to weaken Michigan\u27s capacity to respond quickly and effectively to crises, both natural and human made. Minority and low-income populations, already disadvantaged, suffer the burden of the inequitable social, environmental, and economic injustices that have culminated in previously unacknowledged levels. Since 2011, the Michigan State University U.S. Economic Development Administration (EDA) University Center for Regional Economic Innovation (REI) has been leveraging higher education assets in collaboration with distressed regions to support the co-creation, co-implementation, and dissemination of new economic development strategies to build equitable, resilient, and inclusive economies. The Michigan State University Center for Community and Economic Development (CCED) and its Regional Economic Innovation (REI) initiative’s unique approach embraces collaboration to create innovative economic development tools, models, policies, and programs to improve the lives of underserved and historically excluded citizens. The Center addresses these challenges through community engagement, strategic partnerships, and collaborative learning.
Through technical assistance, Student-Led Faculty-Guided (SLFGs) projects, Co-Learning Plans (CLPs), and Innovation Fellowships (IFs), REI and partners co-create innovative and equitable initiatives leading to long-term economic recovery across Michigan. Addressing these issues requires an innovative, community-centered approach that leverages higher education assets. Engaging Michigan’s underserved communities through a collaborative model can drive high-growth entrepreneurship, job creation, and equitable economic development, fostering resilience and long-term prosperity.REI’s approach also emphasizes justice, equity, diversity, inclusion, and belonging (JEDI+B), and draws on the unique strengths of higher education to empower local communities. The model has produced measurable impacts, such as increased investments and strengthened community networks, positioning REI as a replicable model for regional economic innovation