Impressive Words: Linguistic Predictors of Public Approval of the U.S. Congress

Effect of online versus in-person clinical experiences on nursing student's competency development: A cross-sectional, quasi-experimental design

BACKGROUND

Clinical experience is an important component of nursing education, yet placements in agencies are hard to secure, and evaluation of outcomes challenging. The shift to virtual, online clinical courses during the pandemic created the conditions of a natural experiment.

OBJECTIVES

To compare differences in outcomes between an in-person and online design for a family and community health clinical course.

DESIGN

Quasi-experimental, 2-group, cross-sectional study.

METHODS

Competency evaluations were reviewed from a baccalaureate nursing program in Canada from 19 students who completed an in-person clinical, and 32 students who completed an online clinical. Quantitative analysis compared competencies achieved, interest in community health nursing, and linguistic analysis of unstructured narratives using natural language processing.

RESULTS

There are differences in competency evaluations for in-person versus online community clinical courses, and potential implications for future interest in community health. Natural language processing detected differences in content and psychological processes between the two groups.

CONCLUSIONS

Nursing programs could apply this methodology to track impact of changes to clinical course design on achievement of competencies. There are important differences in outcomes between online and in-person clinical courses.

Towards gender-inclusive job postings: A data-driven comparison of augmented writing technologies

Job advertisements are often worded in ways that might pose discrimination risks leading to the exclusion of certain groups of applicants, particularly in relation to their gender. Especially in male-dominated professions or leadership roles, the specific linguistic formulation of job postings acquires relevance if more women are to be attracted to apply. Various technologies have emerged that offer automated text screening, some of them even suggesting alternative formulations to increase gender inclusivity. In this study we analyze four software providers on the German market using a corpus of ∼160, 000 job ads from three different platforms. We identify the relevant social psychological research on gender and language that is at the scientific core of these technologies. We show that, despite sharing a common foundation, the four tools assess the potential for exclusion in job postings in a considerably divergent way on multiple levels of comparison. We discuss the levers in the software pipeline of all four technologies, as well as the potential effect of certain implementation decisions, such as string-based vs. semantic approaches to computational processing of natural language. We argue that the ‘technological translation’ of research is extremely involved and further studies of its use in practice are needed to assess the potential for more gender equality.

Analysis of the causes of inferiority feelings based on social media data with Word2Vec

Feelings of inferiority are complex emotions that usually indicate perceived weakness and helplessness. A lack of timely and effective interventions may bring serious consequences to individuals with inferiority feelings. Due to privacy concerns, those people often hesitate to seek face-to-face help, but they usually spontaneously share their feelings on social media, which makes social media a good resource for ample inferiority-related data. We randomly selected a sample of posts indicating inferiority feelings to explore the causes of inferiority. Through language analysis and natural language processing, we constructed a Word2Vec model of inferiority based on social media data and applied it to the cause analysis of inferiority feelings. The main causes of inferiority feelings are personal experience, social interaction, love relationship, etc. People feeling inferior about their personal experiences usually are largely influenced by their ways of thinking and life attitudes. Social and emotional factors overlap somewhat in the development of inferiority. In love relationships, males are more prone to inferiority feeling than females. These findings will help relevant institutions and organizations better understand people with inferiority feelings and facilitate the development of targeted treatment for those with potential self-esteem problems.

Detecting depression stigma on social media: A linguistic analysis

BACKGROUND

Efficient detection of depression stigma in mass media is important for designing effective stigma reduction strategies. Using linguistic analysis methods, this paper aims to build computational models for detecting stigma expressions in Chinese social media posts (Sina Weibo).

METHODS

A total of 15,879 Weibo posts with keywords were collected and analyzed. First, a content analysis was conducted on all 15,879 posts to determine whether each of them reflected depression stigma or not. Second, using four algorithms (Simple Logistic Regression, Multilayer Perceptron Neural Networks, Support Vector Machine, and Random Forest), two groups of classification models were built based on selected linguistic features; one for differentiating between posts with and without depression stigma, and one for differentiating among posts with three specific types of depression stigma.

RESULTS

First, 967 of 15,879 posts (6.09%) indicated depression stigma. 39.30%, 15.82%, and 14.99% of them endorsed the stigmatizing view that "People with depression are unpredictable", "Depression is a sign of personal weakness", and "Depression is not a real medical illness", respectively. Second, the highest F-Measure value for differentiating between stigma and non-stigma reached 75.2%. The highest F-Measure value for differentiating among three specific types of stigma reached 86.2%.

LIMITATIONS

Due to the limited and imbalanced dataset of Chinese Weibo posts, the findings of this study might have limited generalizability.

CONCLUSIONS

This paper confirms that incorporating linguistic analysis methods into online detection of stigma can be beneficial to improve the performance of stigma reduction programs.

Eheglück durch „Wenn“ und „Aber“?

Zusammenfassung. Theoretischer Hintergrund: Die Qualität der partnerschaftlichen, verbalen Kommunikation ist ein entscheidender Faktor für Trennung und Scheidung. Durch Konjunktionen können Satzteile sinnvoll miteinander verknüpft und Beziehungen zwischen ihnen hergestellt werden. Fragestellung: Lässt sich anhand von in Konfliktgesprächen verwendeten Konjunktionen die Scheidungswahrscheinlichkeit und die Partnerschaftszufriedenheit von Ehepaaren nach fünf und 25 Jahren vorhersagen? Methode: Zur Untersuchung dieser Hypothesen wurden 172 Transkripte von N = 65 zufriedenen Ehepaaren linguistisch ausgewertet. Ergebnisse: Mithilfe von logistischen und linearen Regressionsmodellen konnte gezeigt werden, dass die verwendeten Konjunktionen einen signifikanten Prädiktor (Schutzfaktor) für die Vorhersage der Scheidungswahrscheinlichkeit darstellen. Zur Vorhersage der Partnerschaftszufriedenheit eigneten sie sich hingegen zu keinem der beiden Untersuchungszeitpunkte. Schlussfolgerungen: Insgesamt stellte der Konjunktionsgebrauch einen Indikator für komplexe soziale Interaktionsprozesse dar.