Brianna M. White

Background: Pregnancy-related weight gain is both physiologically complex and socially charged, and online discussions often combine clinical information with implicit emotional distress. Although automated sentiment analysis could support research on pregnancy-related discourse, model performance in this context remains inadequately characterized relative to expert human judgment. Objective: This study aimed to benchmark contemporary sentiment classification models on pregnancy-related weight discussions, with emphasis on agreement with expert consensus, interpretive ambiguity, task adaptation, and deployment across posts and comments. Methods: We curated 15,619 Reddit posts and comments using a large language model (LLM)–guided keyword expansion strategy and established an adjudicated consensus reference set of 200 posts. We evaluated 10 models on a fixed held-out test set (N=150) using aligned text inputs, weighted performance metrics, and 1000-resample bootstrap CIs. RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) and BERTweet (Bidirectional Encoder Representations from Transformers for Tweets) were additionally evaluated in a 5-fold fine-tuning sensitivity analysis. Gemini-3-Flash (Google DeepMind [Google LLC]) and Generative Pretrained Transformer (GPT)–OSS (OpenAI) were then applied to 400 unlabeled posts and 15,019 comments with intermodel agreement assessment. To expand expert-labeled coverage, a separate 106-post annotation extension was used for secondary evaluation of the 2 models carried forward to post-level corpus inference, and cross-source recoverability of the API-derived retrieval was assessed using Arctic Shift (Arthur Heitmann). Results: The 2 primary experts (FAK and BMW) achieved 59.0% preadjudication agreement (Cohen κ=0.344). In the fixed held-out comparison, Gemini-3-Flash had the numerically highest weighted F ₁ -score of 0.662 (κ=0.478), followed by GPT-OSS ( F ₁ -score=0.645; κ=0.436), with overlapping CIs across leading models. Under the harmonized direct-argmax decision rule with minimally processed text, BERTweet achieved an F ₁ -score of 0.623 (κ=0.369), and RoBERTa achieved an F ₁ -score of 0.605 (κ=0.339). In the separate fine-tuning sensitivity analysis, the mean cross-validated F ₁ -score was 0.708 (SD 0.087) for RoBERTa and 0.616 (SD 0.079) for BERTweet. Among Gemini-3-Flash errors, 55.8% (29/52) occurred in posts on which the 2 primary experts disagreed. In the 106-post annotation extension, primary-expert agreement was 64.2% (68/106; κ=0.367); Gemini-3-Flash and GPT-OSS achieved weighted F ₁ -score values of 0.633 and 0.680, respectively, and both identified 56 of 58 expert-negative posts in an exploratory human-review prioritization analysis. All 714 API-retrieved post IDs were independently recoverable through Arctic Shift. Intermodel agreement was 92.3% for posts (κ=0.797; n=400 valid pairs) and 46.3% for comments (κ=0.102; n=1500). Conclusions: No single model architecture was uniformly superior, and performance depended on the adaptation regime, annotation ambiguity, and discourse type. These findings define important boundary conditions for sentiment analysis of naturalistic maternal-health discourse: broad polarity classification can support corpus prioritization and hypothesis generation but is insufficient as a stand-alone measure of patient experience.

Background: Navigation programs for patients with cancer improve access and continuity of care, yet their digital transformation is often limited by poor usability and inadequate uptake. Applying user-centered and human-centered design (UCD/HCD) principles may close this gap, but the extent to which such design methods are used and evaluated in oncology navigation tools remains unclear. Objective: This scoping review identifies how UCD/HCD principles have been, and should be, applied in developing and implementing digital health tools for navigation for patients with cancer. Methods: A scoping review was conducted following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Joanna Briggs Institute guidance. A total of 7 databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, Embase, ACM Digital Library, and CINAHL) were searched for English-language articles published between January 2015 and July 2025. Eligible studies reported original, peer-reviewed research on digital or mobile health interventions linked to cancer navigation and documented at least 1 UCD/HCD activity. Two reviewers independently screened records and charted data on context, target users, functions, tool modality, design phase, methods, and outcomes. Findings were synthesized descriptively and thematically. Results: A total of 36 studies met the inclusion criteria. Findings were organized into 4 domains: study characteristics, navigation functions and digital modalities, design processes and methods, and UCD/HCD application. Iterative prototyping and usability testing were the most common, while participatory design and implementation evaluation were underused. Conclusions: UCD/HCD approaches enhance usability and patient relevance of digital cancer navigation tools. However, their application remains limited across cancer types, regions, and functions. Broader stakeholder participation and evaluation beyond usability are needed to strengthen coordination, equity, and sustainability in cancer care.

Background: In fall 2021, Tennessee school districts faced heightened debates over COVID-19 mitigation amid rising cases, limited vaccination availability, and widespread misinformation. School board meetings (SBMs) served as pivotal decision-making forums influencing district policies. This study investigated perceptions and misinformation regarding the COVID-19 mask mandate at SBMs held within 6 of Tennessee’s largest school districts. With widespread debate over pandemic measures, including mask use in schools, understanding community sentiments is crucial for guiding public health policies. Objective: This study aimed to investigate the viewpoints of parents or caregivers and teachers regarding COVID-19 safety protocols, particularly the mask mandate, and to identify the misinformation circulating within SBMs. Methods: Participants’ commentaries were extracted from 6 SBM recordings that were publicly uploaded to YouTube from August through September 2021. The data were examined qualitatively to capture themes related to concerns, support, and misinformation. Inductive thematic analysis was conducted using transcripts generated via Microsoft Azure speech-to-text and manually verified. Results: Many parents or caregivers gave personal accounts of how the pandemic had impacted them, their children, and their communities, describing significant comorbidities, adverse psychosocial impacts, mental health disorders, learning difficulties, and worsening socioeconomic and educational disparities. Six thematic domains emerged: (1) perceived effects of the COVID-19 pandemic on children, teachers, and parents or caregivers, including psychosocial distress, learning disruptions, and burnout; (2) perceived effects of mask mandates on children, particularly concerns regarding physical health and psychosocial well-being; (3) perceived government overreach and legal objections to COVID-19 mitigation mandates; (4) tensions between personal liberty, religious beliefs, and collective responsibility in masking decisions; (5) circulation of misinformation and conflicting guidance regarding mask safety and effectiveness; and (6) institutional strain, social tensions, and hostility directed toward school officials alongside educator burnout. Conclusions: Perspectives on COVID-19 mitigation varied widely across meeting participants, highlighting the need for health officials and policymakers to engage in proactive health promotion strategies. Strengthening public health communication, misinformation mitigation, and institutional support for teachers will be essential to ensuring safe and effective learning environments during future public health crises.

Background: Artificial intelligence (AI) is rapidly reshaping oncology, offering advancements in clinical care and patient support. A growing area of interest is the integration of empathic AI: systems integrating clinical precision with emotional intelligence to support medical decision-making and the emotional and psychosocial well-being of patients and caregivers. Objective: This review aimed to explore the role of empathic AI in cancer care, with a focus on its applications in patient education, clinician support, and emotional care. It also evaluated the ethical, cultural, and implementation challenges associated with its integration into clinical practice in oncology. Methods: A systematic search of literature was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Peer-reviewed papers published between January 2018 and January 2025 were identified through a search of PubMed, Scopus, and IEEE Xplore, and citation tracking. Eligible studies focused on applications of empathic AI in oncology. A total of 44 studies were included and analyzed thematically across 3 core clinical domains: tailored communication and education, diagnostics and care plan optimization, and emotional and psychosocial support. Results: Empathic AI demonstrates the potential to improve cancer care by enhancing patient education, clinical decision-making, and emotional support. Common applications include personalized education for patients and providers, support for diagnostic and therapeutic decisions, and tools designed to recognize and respond to patient distress. Several studies noted improved patient engagement and reduced clinician burden. However, concerns were identified regarding overreliance on AI systems, cultural insensitivity, and patient privacy. Conclusions: Empathic AI represents a promising advancement in patient-centered oncology, integrating emotional intelligence into clinical care. Its successful implementation will require careful attention to ethical, cultural, and clinical considerations to ensure health equity, trust, and safety in AI-assisted cancer care.

BACKGROUND Pregnancy-related weight gain is both physiologically complex and socially charged, resulting in online discourse where clinical facts often blend with implicit emotional distress. While automated sentiment analysis could support maternal health surveillance, model performance in this emotionally complex context remains inadequately characterized relative to expert human judgment. OBJECTIVE To benchmark contemporary sentiment classification models on pregnancy-related weight discussions, with emphasis on agreement with expert consensus, robustness under interpretive ambiguity, and practical deployment feasibility. METHODS We curated 15,619 Reddit posts and comments using an LLM-guided keyword expansion strategy. A "Gold Standard" was established through expert adjudication of 200 posts. We evaluated 10 models (3 commercial and 5 open-source LLMs, 2 MLMs) across zero-shot and few-shot configurations. Performance was measured using weighted F1-score and Cohen’s κ relative to adjudicated and pre-adjudication human baselines. Validated models were applied to 15,019 unlabeled comments to assess scalability and inter-model agreement. RESULTS Expert annotators achieved moderate pre-adjudication agreement (κ = 0.34), while expert–gold standard agreement establishes an empirical performance ceiling. The top-performing model (Gemini-3-Flash) achieved F1 = 0.66 and κ = 0.48, exceeding pre-adjudication inter-annotator agreement by 0.14. Moderate-parameter open-source models (e.g., GPT-OSS: F1 = 0.65, κ = 0.44) achieved comparable performance. Classification errors concentrated at the Neutral–Negative boundary. Inter-model agreement on unlabeled posts was high (κ = 0.80) but dropped sharply for comments (κ = 0.10) in a stratified sample, indicating a significant discourse-type dependency. CONCLUSIONS Sentiment classification in pregnancy weight gain is fundamentally constrained by human interpretive disagreement. Open-source LLMs can substitute for commercial systems on narrative-style discourse, but performance remains bounded by task-intrinsic ambiguity. Model selection should prioritize human-relative agreement and discourse-type alignment over marginal performance gains.

BACKGROUND Navigation programs for patients with cancer improve access and continuity of care, yet their digital transformation is often limited by poor usability and inadequate uptake. Applying user-centered and human-centered design (UCD/HCD) principles may close this gap, but the extent to which such design methods are used and evaluated in oncology navigation tools remains unclear. OBJECTIVE This scoping review identifies how UCD/HCD principles have been, and should be, applied in developing and implementing digital health tools for navigation for patients with cancer. METHODS A scoping review was conducted following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Joanna Briggs Institute guidance. A total of 7 databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, Embase, ACM Digital Library, and CINAHL) were searched for English-language articles published between January 2015 and July 2025. Eligible studies reported original, peer-reviewed research on digital or mobile health interventions linked to cancer navigation and documented at least 1 UCD/HCD activity. Two reviewers independently screened records and charted data on context, target users, functions, tool modality, design phase, methods, and outcomes. Findings were synthesized descriptively and thematically. RESULTS A total of 36 studies met the inclusion criteria. Findings were organized into 4 domains: study characteristics, navigation functions and digital modalities, design processes and methods, and UCD/HCD application. Iterative prototyping and usability testing were the most common, while participatory design and implementation evaluation were underused. CONCLUSIONS UCD/HCD approaches enhance usability and patient relevance of digital cancer navigation tools. However, their application remains limited across cancer types, regions, and functions. Broader stakeholder participation and evaluation beyond usability are needed to strengthen coordination, equity, and sustainability in cancer care.

PURPOSE: Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based models have been commonly used, explainable machine learning models may offer enhanced predictive accuracy and deeper insights into the factors influencing LC mortality. METHODS: This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model performance was evaluated using R-squared and root mean squared error (RMSE). Shapley Additive Explanations (SHAP) values were used to determine variable importance and their directional impact. Geographic disparities in LC mortality were analyzed through Getis-Ord (Gi*) hotspot analysis. RESULTS: The RF model outperformed both GBR and LR, achieving an R ² value of 41.9% and an RMSE of 12.8. SHAP analysis identified smoking rate as the most important predictor, followed by median home value and the percentage of the Hispanic ethnic population. Spatial analysis revealed significant clusters of elevated LC mortality in the mid-eastern counties of the United States. CONCLUSION: The RF model demonstrated superior predictive performance for LC mortality rates, emphasizing the critical roles of smoking prevalence, housing values, and the percentage of Hispanic ethnic population. These findings offer valuable actionable insights for designing targeted interventions, promoting screening, and addressing health disparities in regions most affected by LC in the United States.

Early life experiences are crucial for health and well-being, influencing physical, emotional, and social development throughout the lifespan. Recent research shows that Positive Childhood Experiences (PCEs), such as access to supportive environments and relationships, can help mitigate the effects of Adverse Childhood Experiences (ACEs). In this context, we argue that digital health and AI technologies can offer innovative solutions by enabling the screening and promotion of PCEs through personalized interventions.

Background: Exposures to both negative and positive experiences in childhood have proven to influence cardiovascular, immune, metabolic, and neurologic function throughout an individual’s life. As such, adverse childhood experiences (ACEs) could have severe consequences on health and well-being into adulthood. Objective: This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth. Methods: Several databases were searched for studies published from August 2017 to August 2022. Selected studies (1) explored the relationship between digital health interventions and mitigation of negative health outcomes associated with mental health in childhood and adolescence and (2) examined prevention of ACE occurrence associated with mental illness in childhood and adolescence. A total of 18 search papers were selected, according to our inclusion and exclusion criteria, to evaluate and identify means by which existing digital solutions may be useful in mitigating the mental health consequences associated with the occurrence of ACEs in childhood and adolescence and preventing ACE occurrence due to mental health consequences. We also highlighted a few knowledge gaps or barriers to DHT implementation and usability. Results: Findings from the search suggest that the incorporation of DHTs, if implemented successfully, has the potential to improve the quality of related care provisions for the management of mental health consequences of adverse or traumatic events in childhood, including posttraumatic stress disorder, suicidal behavior or ideation, anxiety or depression, and attention-deficit/hyperactivity disorder. Conclusions: The use of DHTs, machine learning tools, natural learning processing, and artificial intelligence can positively help in mitigating ACEs and associated risk factors. Under proper legal regulations, security, privacy, and confidentiality assurances, digital technologies could also assist in promoting positive childhood experiences in children and young adults, bolstering resilience, and providing reliable public health resources to serve populations in need.