How Much Do We Really Know About Employee Resilience?
Bibliographic record
Abstract
Past research purporting to study employee resilience suffers from a lack of conceptual clarity about both the resilience construct and the methodological designs that examine resilience without ensuring the occurrence of significant adversity. The overall goal of this article is to address our contemporary understanding of employee resilience and identify pathways for the future advancement of resilience research in the workplace. We first address conceptual definitions of resilience both inside and outside of industrial and organizational psychology and make the case that researchers have generally failed to document the experience of significant adversity when studying resilience in working populations. Next, we discuss methods used to examine resilience, with an emphasis on distinguishing the capacity for resilience and the demonstration of resilience. Representative research is then reviewed by examining self-reports of resilience or resilience-related traits along with research on resilient and nonresilient trajectories following significant adversity. We then briefly address the issues involved in selecting resilient employees and building resilience in employees. The article concludes with recommendations for future research studying resilience in the workplace, including documenting significant adversity among employees, assessing multiple outcomes, using longitudinal designs with theoretically supported time lags, broadening the study of resilience to people in occupations outside the military who may face significant adversity, and addressing the potential dark side of an emphasis on resilience.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".