The emergence of team compassion: Theoretical implications and practical interventions
Bibliographic record
Abstract
With the recent experiences involving COVID-19, there is a growing need for organisations to better understand compassion in addressing employees’ suffering and boosting their well-being. Particularly, as teamwork is becoming ubiquitous, organisational scholars have identified positive benefits of compassion at the team level such as improving communication, decreasing interpersonal conflicts and boosting team effectiveness. Using a multilevel theoretical framework in reviewing compassion research, this article advances our understanding of team-level compassion by elucidating the processes through which individual-level compassion gives rise to team-level compassion. First, we delineate composition and compilation models of the emergence of team compassion and review empirical studies with respect to the two models. Second, we explain three social mechanisms in teams – social learning, emotional contagion and reciprocity – that shape the emergence of team compassion. Finally, we discuss interventions that can facilitate the emergence of team compassion and offer practical guidance for managers seeking to foster team compassion. JEL Classification: D23, I31
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".