How Much Do We Really Know About Employee Resilience?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it