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An Empirical Examination of the Dependability of the Self-Report and Informant Forms of the BFI, PID-5-SF, and PiCD
No full textDependability is critical for the reliability and validity of trait(-like) measures. Therefore, a detailed understanding of the dependability of self-report and informant measures of non-pathological and pathological personality traits is critical to determine how transient error may influence our knowledgebase of personality. The present study examined the dependability of the self-report and informant BFI, PID-5-SF, and PiCD. Furthermore, it tested if the self-report and informant forms are differentially dependable. 385 MTurkers completed self-report and informant-report BFI, PID-5-SF, and PiCD measures twice over a 1-week retest. Self-report measures frequently demonstrated higher dependability than informant report measures. Notably, this pattern predominantly emerged for traits comprising the positive and negative affect continua, suggesting content may influence susceptibility to error. Benchmark comparisons indicated the self-report and informant PID-5-SF and PiCD are modestly less dependable than the self-report BFI, suggesting suboptimal dependability. Lastly, included personality measures were generally less dependable than physical and demographic characteristics, with the exception of race. Overall, the dependability of the self-report and informant BFI, PID-5-SF, and PiCD may be acceptable; however, they demonstrate important cross-method and cross-measure differences in dependability, which must be carefully considered when interpreting research using these measures
Voice of My People: An Expression of Welcome and Liturgical Justice for Afrodiasporic People in North American Catholicism
Get PDFMusic has always been central to the lives of Black people. The Black religious experience is even more profound when expressed through music. It is therefore no happenstance that music is of great import as pertains to the spiritual lives of Black people, including Black Catholics. Faith is expressed through culture. It is through culture that the faith is realized and known. In this, is the essence of inculturation.
Anscar Chupungco states that if not inculturated, the liturgy of the local Church will remain at the periphery of people’s cultural experience. For this reason, it is imperative that Afrodiasporic music be included in the liturgy for the faith formation of Black Catholics, as an act of hospitality and liturgical justice. In this thesis, I use my recent Mass setting, Voice of My People, to support various assertions made in this thesis. Multiple methodologies—including musicological, historical, ethnographic, and artist intervention—are used to support various hypotheses pertaining to inculturation, multiculturalism, and liturgical justice. Ethnographic research was conducted at six parishes across the United States yielding quantitative and qualitative data. As the United States becomes more ethnically diverse, so too, must the American Catholic Church respond by becoming more welcoming to people of various cultures.
This thesis situates Black Catholic music as an important facet of Black sacred music more broadly in a nuanced conversation about Black theology and spirituality within a Catholic context
Smarter Disease Detection From Electronic Health Record Data: An End-To-End AI-Augmented Pipeline For Computable Phenotyping
No full textElectronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.
We developed an end-to-end, multimodal phenotyping pipeline that integrates structured EHR data with LLM-derived insights from unstructured clinical notes to improve disease classification. Using diabetes phenotyping as a proof-of-concept, the framework begins with a logistic-LASSO model trained on structured EHR features to generate patient-level predicted probabilities. Initially, augmentation targeted cases with intermediate probabilities—where uncertainty was highest and structured data alone was insufficient for accurate classification—by prompting an LLM to classify disease status from retrieved clinical notes. LLM-derived classifications were added to the structured predictor set as a three-level categorical variable indicating whether the patient was (1) not flagged for LLM augmentation, (2) LLM-classified as disease-absent, or (3) LLM-classified as disease-present. Compared with both a traditional rule-based phenotype and the structured-only logistic-LASSO, this probability-thresholding approach improved all measured performance metrics, demonstrating the added value of targeted unstructured data insights. Nonetheless, reliance on manually defined thresholds limited generalizability.
To address these limitations, we advanced to an ensemble-guided LLM-augmentation strategy. Here, a diverse set of base learners trained on structured data flagged cases for augmentation based on disagreement, eliminating subjective thresholds and offering an objective, adaptable selection criterion. This improved identification of patients most likely to benefit from LLM augmentation, and the resulting ensemble-guided, LLM-augmented logistic-LASSO outperformed the threshold-based method.
We evaluated this approach on both diabetes and peripheral artery disease (PAD), two phenotypes with distinct clinical presentations and documentation patterns. Ensemble disagreement proved to be a phenotype-agnostic and effective criterion for targeted augmentation. Compared with full cohort augmentation, this strategy prompted the LLM for only 10\% of patients on average, yet achieved comparable—or occasionally superior—performance, delivering significant gains in cost-efficiency, scalability, and sustainability.
Finally, we incorporated a human-in-the-loop (HIL) mechanism for targeted label correction and identification of high-quality examples for LLM self-improvement. Iterative fine-tuning with expert-reviewed cases consistently improved sensitivity, negative predictive value, and overall accuracy across development, internal validation, and external validation cohorts. Together, these findings demonstrate that targeted, uncertainty-guided LLM integration can deliver high performance while preserving portability across settings.
Key contributions include: (1) a transparent, interpretable, and uncertainty-aware method for integrating LLMs into phenotyping pipelines; (2) an ensemble disagreement metric as a scalable and objective patient selection strategy for augmentation; and (3) a HIL-driven self-improvement process to refine performance. Limitations include the cost of LLM inference, the site-specific nature of self-improvement gains, and the need for adaptation to new clinical domains. Overall, this framework offers a practical, clinician-friendly pathway for enhancing disease detection from EHR data—balancing innovation with interpretability and adaptability
The Duality of Perception: Unveiling the Hidden Through Visible Forms
No full textThis MFA thesis, The Duality of Perception: Unveiling the Hidden through Visible Form, involves layering paints on shaped wood panels and paper, using the traditional technique of tezhib (gold illumination) and geometric art. Through these layers, I aim to challenge the viewers’ perceptions by simultaneously revealing and concealing elements within the work. Symbols drawn from Islamic traditions such as pomegranates, birds, and ribbons are incorporated in the paintings to carry both cultural and personal significance, evoking my connections to spiritual and physical realms.
Rooted in the principles of Islamic art and informed by spiritual themes such as paradise, divine harmony, and interconnectedness, this thesis investigates the duality of dhahir (the visible) and batin (the hidden) within my artistic practice. My exploration of these themes is shaped by my lived experiences, including my Pakistani traditions, memories, and values passed down through my family heritage, which together enrich the visual narrative and deepen the dialogue between what is seen on the surface and the emotions and meanings that lie beneath the imagery.
In my work, geometry and abstraction serve not only as aesthetic tools but as portals for transcendence, inviting contemplation and inner reflection. By exploring the hidden meanings within visible forms, my art becomes a means of unveiling the mysteries that lie beneath the surface
Do Designated Market Makers Facilitate Earnings News Discovery?
No full textAs markets replace contractual liquidity providers (designated market makers; DMMs) with voluntary liquidity provision through cutting-edge technology, we investigate how this affects price discovery. Research suggests that endogenous liquidity provision is not always optimal. We investigate how DMMs affect the incorporation of earnings news into prices. Using a regression discontinuity design, we show that increased DMM participation facilitates earnings news discovery—lower JUMP, lower Synchronicity, and higher Future Earnings Response Coefficient. Greater DMM participation associates with improved liquidity, and induces greater informed trading as evidenced by more short selling on negative news and increased EDGAR and Bloomberg search activity before earnings announcements. Our results highlight an important and hitherto overlooked effect of modern technology on processing earnings information
CSEY Data Advocacy: Reducing Harm through Data - CSEY Survivors
Get PDFThis presentation will focus on the crucial overlap of time management and protection for survivors during data collection. Time management is an essential part of any successful organization, especially when working with vulnerable populations, such as survivors of human trafficking, and facing tight schedules and budgets. It is time- consuming to read through blocks of narratives to find necessary details on survivors, but more importantly, survivors can be harmed by being forced to relive their trauma during data collection. On the other hand, narratives written by advocates can harm survivors if subpoenaed. Documentation data systems that gather quantitative information and tell the story through data can be used to reduce harm to survivors of human trafficking. Data can be used to track survivors’ engagement, connections, needs, and feelings of safety; and to determine efficiency of implementation of intervention strategies. This presentation offers an organizational example of a documentation data system for providing care and protecting survivors of human trafficking
A Participatory Approach to Case Management: Co-Creation of Effective Anti-Trafficking Programming Based on the Self-Identified Strengths, Needs, and Priorities of Survivors
Get PDFSome estimates claim the existence of 50 million victims of human trafficking and the U.S. Department of State suggests that less than 1% of survivors received services to prevent further exploitation. This presentation shares processes for co-creating knowledge with case management tools that empower survivors while gathering data to improve their care, assist service providers in effectively targeting supportive services, and strengthen anti-trafficking initiatives. Findings will be shared from Freedom Lifemap, a self-assessment used by human trafficking survivors to identity their own strengths, needs, and priorities to prevent the risk of re-exploitation and attain lasting freedom. Global patterns in data are explored from self-assessments conducted in Southeast Asia, Latin America, East Africa, and the U.S. Implications for this research include opportunities for collaboration within and beyond the anti-trafficking sector to identify and scale effective programming