BACKGROUND Large language models are increasingly deployed in mental health applications, yet growing evidence suggests they encode algorithmic biases that influence clinical outputs. Because these models now mediate patient-facing decisions, such biases carry the potential for direct harm. Whether they systematically affect psychiatric diagnosis across demographic groups remains underexplored. OBJECTIVE To examine whether large language models (LLMs) exhibit implicit demographic biases when generating psychiatric diagnoses. METHODS We developed 1,152 synthetic clinical vignettes using a matched-pair design that manipulated gender, race/ethnicity, age, socioeconomic status, English proficiency, and urbanicity while holding clinical content constant. Vignettes were divided into control (unambiguous anorexia nervosa) and ambiguous conditions designed to permit differential diagnosis. Ten LLM configurations across five model families were tested. RESULTS Control vignettes produced near-unanimous anorexia nervosa diagnoses (M = 100.0%), while ambiguous vignettes elicited greater variability (M = 23.6%). Inter-model agreement was moderate for ambiguous vignettes (Fleiss' κ = 0.410, 95% CI: 0.397–0.422). Mixed-effects logistic regression with LLM as a random intercept revealed significant demographic biases: Black patients were over six times more likely to receive a major depressive disorder diagnosis than White patients with identical presentations (OR = 6.09, 95% CI: 5.13–7.24), Latine patients were over nine times more likely (OR = 9.57, 95% CI: 8.00–11.45), and Asian patients were nearly three times more likely to receive an anorexia nervosa diagnosis (OR = 2.88, 95% CI: 2.44–3.42). Female patients were less likely than males to be diagnosed with anorexia nervosa (OR = 0.43, 95% CI: 0.37–0.49). CONCLUSIONS These findings demonstrate that LLMs exhibit systematic demographic biases in psychiatric diagnosis even when clinical content is held constant, revealing measurable patterns that can inform improvements to training data, model architecture, and clinical deployment frameworks.

This study uses sentiment analysis to examine changes in authorial stance over time in the flagship journal of rehabilitation counseling. The corpus linguistic analysis found overall positive sentiment, with higher happiness scores in 2021-2022 compared to 2019-2020. Other emotions remained constant. Implications and recommendations are discussed.

As Taiwan’s population ages, traditional filial piety expectations face modernization challenges, yet few studies examine how emergent adults linguistically negotiate these cultural tensions digitally. This study addresses this gap by analyzing how emerging adults in Taiwan express and reinterpret filial obligations toward aging parents through online discourse. Emerging adults represent a particularly meaningful demographic because they straddle traditional filial norms and modern independence, making their language use a valuable indicator of cultural transition. We analyzed 1976 Dcard posts from 30 discussion threads (2017–2023) using computational linguistics. LIWC-22 assessed emotional expression patterns, while Sketch Engine conducted keyness analysis and collocation mapping around filial care keywords. Posts were compared against Chinese web corpus norms. Quantitative emotion analysis revealed dominant positive emotions (M = 3.93) alongside significant negative emotions (M = 3.30), with anger and sadness exceeding broader Chinese online communication norms. Keyness analysis identified economic concerns as central themes. Collocation analysis around “filial piety” showed associations with “limits”, “willingness”, and “define”, indicating conditional rather than absolute conceptualization. Findings indicate that emerging adults in Taiwan reinterpret filial piety through reciprocal emotional bonds rather than strict hierarchical duty, negotiating traditional expectations with contemporary economic realities and personal autonomy. The implications of these findings highlight how cultural values adapt in response to modernization and digital communication, offering insight into evolving intergenerational relationships and informing future cross-cultural aging and caregiving research.

This pilot study demonstrates language style matching (LSM) as an evaluation tool when examining counseling session discourse transcripts. LSM explores the language style of individuals and whether there is coordination in a dyadic conversation. This study examined the differences between suicidal discourse and general discourse. We employed a cross-sectional corpus linguistic analysis of transcripts of counseling sessions. The corpora were analyzed using the LSM methodology embedded in the Linguistic Inquiry and the Word Count software (LIWC-22). The results showed that LSM between clients and counselors within suicide discourse sessions was not statistically significantly different from those with general counseling content. Additionally, stylistic words did not vary between the two respective groups of dyads. The LSM can be an assessment tool in analyzing transcripts to determine the level of empathy and the therapeutic alliance. Additional basic counseling skills are transferable across different counseling topics.

This study looks at how the relatively new field of threat assessment uses language by analyzing journal abstracts from its flagship peer-reviewed publication (Journal of Threat Assessment Management). We use a fully updated version of keyword analysis that measures keyness as a combination of frequency and dispersion statistics to find the words that best describe the journal's content. The investigation was concluded by comparing the flagship journal to both forensics journals and journals about culture. By analyzing the discourse, language choice, and narratives of the flagship journal, we can learn more about the research being done in the field of threat assessment. Such analyses seek to promote this emerging field by fostering critical thought and exposing blind spots.