Managing Distress and Expressing Compassion in Organizations
Bibliographic record
Abstract
Scholarship from prior decades suggests that as much as a third of the working population experiences emotional distress or suffers from poor psychological health, suggesting that this is an important topic for management scholars. Indeed, existing management scholarship on workplace distress has examined the causes and consequences of distress, as well as the characteristics of individual employees that impact their ability to cope with distressing events at work. Still, there is still much we do not know; in particular, we suggest that the direct experience of distress across workplaces, as well as the organizational and interpersonal factors that can help mitigate employee suffering warrant greater scholarly attention. Further, relatively little attention has been paid to how the distress of others influences us, and how responding with compassion influences both the one suffering and the one trying to help – topics that are key to improving the function of organizations in the future. To consider this intersection of distress and compassion, we have assembled a panel of scholars who study these topics in the context of organizations. Together, these panelists will shed light on the experience of distress in workplaces, as well as how workers manage the distress of others, and the role that compassion plays in mitigating the distress of both primary and secondary sufferers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".