The Power of Negative Affect during the COVID-19 Pandemic: Negative Affect Leverages Need Satisfaction to Foster Work Centrality
Bibliographic record
Abstract
The COVID-19 pandemic has created unprecedented disruptions in organizations and people’s lives by generating uncertainty, anxiety, and isolation for most employees around the globe. Such disruptive context may have prompted employees to reconsider their identification with their work role, defined as work centrality. As such reconsideration may have deep implications, we reasoned that individuals’ affective dispositions would influence work centrality across time during the pandemic. Drawing upon the broaden-and-build theory of positive emotions and the met expectations underpinnings of negative affectivity, we predicted that positive and negative affect would foster, albeit for different reasons, work centrality. Based on self-determination theory, we further expected the fulfilment of the needs for autonomy, relatedness, and competence to enhance the effect of positive and negative affectivity. Based on a three-wave study (N = 379) conducted during the COVID-19 lockdown followed by a reopening of the economy in Canada (i.e., May to July 2020), we found negative affectivity, but not positive affectivity, to drive work centrality over time, and found this effect to be enhanced at high levels of the satisfaction of the needs for autonomy and relatedness. The implications of these results for our understanding of the role of trait affectivity in times of crisis are discussed.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".