Corrigendum: The language of healthcare worker emotional exhaustion: a linguistic analysis of longitudinal survey

[This corrects the article DOI: 10.3389/fpsyt.2022.1044378.].

Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications

The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.

The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey

Importance

Emotional exhaustion (EE) rates in healthcare workers (HCWs) have reached alarming levels and been linked to worse quality of care. Prior research has shown linguistic characteristics of writing samples can predict mental health disorders. Understanding whether linguistic characteristics are associated with EE could help identify and predict EE.

Objectives

To examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.

Design setting and participants

A large hospital system in the Mid-West had 11,336 HCWs complete annual quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE, an open-ended comment box, and an anonymous identifier enabling HCW responses to be linked across years. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of one exploratory and eight a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.

Main outcomes and measures

The frequency of the linguistic categories (word count, first person singular, first person plural, present focus, past focus, positive emotion, negative emotion, social, power) in HCW comments were examined across EE quartiles.

Results

For the 2019 and 2020 surveys, respondents wrote 3,529 and 3,246 comments, respectively, of which 2,101 and 1,418 comments (103,474 and 85,335 words) contained ≥20 words. Comments using more negative emotion (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) predicted lower current and future EE. Past and present focus, first person plural, and social words did not predict EE. Current EE did not predict future language use.

Conclusion

Five linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, offering insights into EE's etiology, consequences, measurement, and intervention. Future use of these findings could include the ability to identify and support individuals and units at high risk of EE based on their linguistic characteristics.

Significant change events in psychodynamic psychotherapy: Is cognition or emotion more important?

OBJECTIVE

Significant change events are helpful moments within a psychotherapy session that have been shown in previous research to relate strongly to outcome. They are special moments and therefore provide rich data for research into understanding therapeutic process. This study investigated clinical and linguistic features of these helpful moments using and comparing both human ratings and computerized text analysis strategies.

METHOD

Significant change events versus non-event passages were studied within 1195 word blocks of transcribed psychotherapy for 20 participants with diagnoses of comorbid depression and personality disorder. Significant events were determined manually by independent raters using the Helpful Aspects of Therapy (HAT) form linked to the Helpful Aspects of Experiential Therapy Content Analysis System (HAETCAS). Mergenthaler's Therapeutic Cycles Model (TCM)-computerized text analysis, identified significant events via linguistic markers. The Linguistic Inquiry and Word Count (LIWC) differentiated emotional and cognitive components.

RESULTS

Significant events included statements reflecting emotional and cognitive awareness and insight, and moments of alliance strengthening. These events were saturated with both positive and negative emotion words, particularly anger and sadness, and more cognitive insight words.

CONCLUSIONS

Significant moments of psychotherapeutic movement featured high therapeutic alliance. There was evidence of the integration or working through of positive and negative emotional content with cognitive insight - meaning both emotion and cognition were important in these interchanges.

PRACTITIONER POINTS

This study found that significant events in therapy were characterized by high levels of both emotional and cognitive language, and alliance strengthening. Linguistic analysis methods provide important data on psychotherapeutic processes which can be useful in guiding clinicians and improving treatment outcomes.

Topic models: a novel method for modeling couple and family text data

Couple and family researchers often collect open-ended linguistic data-either through free-response questionnaire items, or transcripts of interviews or therapy sessions. Because participants' responses are not forced into a set number of categories, text-based data can be very rich and revealing of psychological processes. At the same time, it is highly unstructured and challenging to analyze. Within family psychology, analyzing text data typically means applying a coding system, which can quantify text data but also has several limitations, including the time needed for coding, difficulties with interrater reliability, and defining a priori what should be coded. The current article presents an alternative method for analyzing text data called topic models (Steyvers & Griffiths, 2006), which has not yet been applied within couple and family psychology. Topic models have similarities to factor analysis and cluster analysis in that they identify underlying clusters of words with semantic similarities (i.e., the "topics"). In the present article, a nontechnical introduction to topic models is provided, highlighting how these models can be used for text exploration and indexing (e.g., quickly locating text passages that share semantic meaning) and how output from topic models can be used to predict behavioral codes or other types of outcomes. Throughout the article, a collection of transcripts from a large couple-therapy trial (Christensen et al., 2004) is used as example data to highlight potential applications. Practical resources for learning more about topic models and how to apply them are discussed.