When caring breeds contempt: The impact of moral emotions on healthcare professionals’ commitment during a pandemic
Bibliographic record
Abstract
The novel coronavirus (COVID-19) pandemic is a major heath crisis that continues to impact healthcare organizations worldwide. As infection rates surged, there was a global shortage of personal protective equipment, critical medications, ventilators, and hospital beds, meaning that healthcare professionals faced increasingly difficult workplace conditions. In this conceptual study, we argue these situations can lead to healthcare professionals experiencing moral emotions - defined as specific emotions which relate, or occur in response, to the interest or welfare of others - towards their organizations. This paper explores the three moral emotions of contempt, anger and disgust, and their potential influence on healthcare professionals' workplace commitment in the context of a pandemic. Drawing from the moral emotions and organizational commitment literature, we develop a process model to demonstrate how healthcare professionals' affective and continuous commitment are likely to decrease while, paradoxically, normative, and professional commitment may become amplified. The possible potential for positive outcomes from negative moral emotions is discussed, followed by theoretical and practical contributions of the model, and finally, directions for future research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".