Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

/pdf/a-multi-country-test-of-brief-reappraisal-interventions-on-4k6n28khru.pdf

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

Work history information reflected in resumes and job application forms is commonly used to screen job applicants; however, there is little consensus as to how to systematically translate information about one's work-related past into predictors of future work outcomes. In this article, we apply machine learning techniques to job application form data (including previous job descriptions and stated reasons for changing jobs) to develop interpretable measures of work experience relevance, tenure history, and history of involuntary turnover, history of avoiding bad jobs, and history of approaching better jobs. We empirically examine our model on a longitudinal sample of 16,071 applicants for public school teaching positions, and predict subsequent work outcomes including student evaluations, expert observations of performance, value-added to student test scores, voluntary turnover, and involuntary turnover. We found that work experience relevance and a history of approaching better jobs were linked to positive work outcomes, whereas a history of avoiding bad jobs was associated with negative outcomes. We also quantify the extent to which our model can improve the quality of selection process above the conventional methods of assessing work history, while lowering the risk of adverse impact. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Using Machine Learning to Translate Applicant Work History into Predictors of Performance and Turnover

Positive psychology approaches have been shown to play a vital role in protecting mental health in times of challenge and are, therefore, important to include when studying the psychological outcom...

/pdf/collective-wellbeing-and-posttraumatic-growth-during-covid-5c27s8lhq3.pdf

Collective wellbeing and posttraumatic growth during COVID-19: how positive psychology can help families, schools, workplaces and marginalized communities

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From Words To Numbers Narrative Data And Social Science

Positive organizational scholarship has led to a growing interest in the critical role of positive emotions for the lives of both workers and organizations. We review and integrate the different pe...

Positive Emotions at Work

Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often...

Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations:

Supplemental Material for Development and Validation of the Mechanisms of Engagement in the Arts and Humanities Scales

Personality Affect Construal Theory: A Model of Personality and Affect in the Workplace

Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.

Stuti Thapa

Papers

Positive Emotions at Work

Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations:

Supplemental Material for Development and Validation of the Mechanisms of Engagement in the Arts and Humanities Scales

Personality Affect Construal Theory: A Model of Personality and Affect in the Workplace

Experience Sampling Methodology: Conceptual and Technological Advances for Understanding and Assessing Variability in Well-being Research