Ana Cleveland

Objective: Medical Concept Normalization (MCN) maps informal health phrases to formal clinical concepts. It is a critical task for pharmacovigilance, patient record analysis, and health-related text mining. Extensive MCN research has mainly relied on single-source datasets and overlooked data quality (DQ) issues. This study aims to develop a quality-aware data fusion framework for MCN using Large Language Models (LLMs). Methods: The methods consist of DQ evaluation and enhancement, LLM-based data augmentation, and multi-source MCN dataset fusion using enriched concept-phrase pairs. We analyze six widely used MCN datasets—AskAPatient, CADEC, COMETA, PsyTAR, TwADR-S, and TwiMed—each collected from social media and mapped to SNOMED-CT. Our evaluation metrics include correctness, concept validity, coverage, semantic variety, and class imbalance. For DQ enhancement and data augmentation, we use Gemini for zero-shot and few-shot learning to increase the semantic variety and phrase count for rare concepts. We then perform data fusion based on shared medical concepts. Results: The DQ evaluation reveals substantial issues, including incorrect mappings, invalid concepts, low-variety redundant phrases, and long-tail concept-phrase distribution. After augmentation, phrase counts increase by 172.1%, and by 450.0% after fusion. To directly investigate model performance improvement on rare cases, we introduce concept-level macro metrics. SapBERT trained on the fused dataset achieves significant gains in accuracy, recall, precision, and F1 over single-source baselines, up to 30.88%.Conclusion: Our study finds that existing MCN benchmarks present data quality issues and underexplored data fusion potential. Data quality enhancement and LLM-based controlled-variety data augmentation help alleviate overlapping phrases and long-tail issues. Moreover, quality-aware data fusion can expand conceptual coverage, improving MCN performance. This work highlights data quality evaluation and fusion strategies are effective in advancing MCN. We hope these contributions could further reduce MCN dataset development costs, support more reliable biomedical text mining, and strengthen downstream applications. Availability: https://github.com/yhZHOU515/DataFusion4MCN

Study Background The study aims to investigate the sustainable health behaviors of underrepresented ethnic minority communities in Pakistan. Pakistan is a multiethnic and multilingual country that hosts national and international populations due to its unique geographic location. Due to cultural and religious imperialism, some ethnic communities have received more attention than others (Alizai, 2021). As a result, minority communities lacking political, economic, social, and religious standing often face marginalization. Underrepresented ethnic groups such as the Baloch, Tajiks, Hazaras, Persians, and Kochis, along with marginalized religious communities such as Hindus, Dalits, Christians, Shias, Parsis, and Deo-Badees, experience discrimination and prejudice in Pakistan (Singha, 2022). Research Methodology To investigate the health-seeking behaviors of displaced Afghan women in Pakistan, this study employs a qualitative research paradigm to collect data. By adopting oral history methods, the research intends to conduct focus group interviews. The research population will be in Quetta, Pakistan, with a sample size of 20 displaced Afghan women. Chatman’s “Small Worlds Theory” will serve as the theoretical lens for data analysis (Chatman, 1991). Thematic analysis will be employed to investigate, assign codes, and generate themes, ultimately leading to conclusions and recommendations. Research Findings The information behaviors of underrepresented Afghan women can help us understand the social and cultural factors that influence health, education, and economic sustainability, especially among displaced women in developing nations. The research aims to recommend reformative changes in Pakistan’s immigration policy that should be gender appropriate and be supportive of the displaced Afghan women in Pakistan.

In this study, the authors analyzed 147 AAHSL member libraries’ COVID-19 research guides to determine the volume and origin of links included. Through stratified sampling based on total enrollment of health sciences academic institutions, 51 eligible AASHL library websites were selected for inclusion in the study. Content from COVID-19 research guides was analyzed, and the origin of each link was categorized. Most AAHSL libraries have at least one COVID-19 research guide, while some have two or more. A total of 8,848 links within examined research guides were visited. Links to academic institutions including universities’ own internal links were most common while news outlets, social media, and international government were linked least. Regarding individual organizations, the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH)were most frequently linked. The overwhelming majority of sampled health sciences libraries use research guides to share COVID-19 information with their users. It is necessary to further investigate how libraries can optimize research guides to benefit their users. These results and conclusions reveal information resource patterns in research guides at health sciences academic libraries and are consistent with those reached by researchers investigating academic libraries’ research guides in 2020.

Background: During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. Objective: The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. Methods: The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. Results: In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. Conclusions: The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.

Erika Love, MLA president and early advocate for research in libraries, died October 8, 2020. Erika held many leadership positions in the profession and received several MLA awards recognizing her contributions. She has been called "the midwife" of MLA's early research initiatives [1].

BACKGROUND During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. OBJECTIVE The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. METHODS The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. RESULTS In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. CONCLUSIONS The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.

Personal branding is an introspective process by which you define yourself professionally, and it can serve as your pathway to professional success. There are six steps to building your personal brand: (1) taking an introspective look, (2) understanding the brand that may already exist, (3) developing your personal brand mantra, (4) crafting your physical footprint, (5) creating your digital footprint, and (6) communicating your message. Your personal brand is a promise of value and performance, and it is necessary to meet the expectations of your audience. Effective brand management is a key component to maintaining a positive brand reputation.