Depending on your own kindness: The moderating role of self-compassion on the within-person consequences of work loneliness during the COVID-19 pandemic.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The coronavirus (COVID-19) pandemic has transformed the way we work, with many employees working under isolating and difficult conditions. However, research on the antecedents, consequences, and buffers of work loneliness is scarce. Integrating research on need for belonging, regulatory loop models of loneliness, and self-compassion, the current study addresses this critical issue by developing and testing a conceptual model that highlights how COVID-related stressors frustrate employees' need for belonging (i.e., telecommuting frequency, job insecurity, and a lack of COVID-related informational justice), negatively impacting worker well-being (i.e., depression) and helping behaviors [i.e., organizational citizenship behavior (OCB)] through work loneliness. Furthermore, we examine the buffering role of self-compassion in this process. Results from a weekly diary study of U.S. employees conducted over 2 months during the initial stage of the pandemic provide support for the mediating role of work loneliness in relations between all three proposed antecedents and both outcomes. In addition, self-compassion mitigated the positive within-person relationship between work loneliness and employee depression, indicating that more self-compassionate employees were better able to cope with their feelings of work loneliness. Although self-compassion also moderated the within-person relationship between work loneliness and OCB, this interaction was different in form from our prediction. Implications for enhancing employee well-being and helping behaviors during and beyond the pandemic are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it