Shock Waves in 29 Countries: Leveraging Personal & Societal Resources to Address Cumulative Shocks
Bibliographic record
Abstract
Cumulative shocks, i.e., exposure to multiple shocks across various domains, pose cognitive demands on individuals. According to Conservation of Resources (COR) theory, these shocks can deplete or conserve cognitive resources. We propose that responses to cognitive demands depend on individual cognitive capabilities. Our study investigates the direct and moderating effects of personal and societal-level cognitive resources on well-being, career satisfaction, and career change intentions—key outcomes for individuals and organizations facing cumulative shocks. We examine personal cognitive resources (career optimism, pessimism, and resilience) alongside societal-level cognitive resources (charismatic/values-based and team-oriented leadership), which can aid in coping with cumulative career shocks. Analyzing data from 7,321 individuals across 29 countries, our results largely support the significant role of cognitive resources, particularly personal cognitive resources. We identify the need to balance career optimism and pessimism and reveal that team-oriented leadership may heighten the negative relationship between cumulative shocks and well-being, perhaps showing the dark side of focusing cognitive resources on the team when change is pushing people to think more about their individual situations. This study extends COR theory and enhances understanding of how diverse cognitive resources that either dampen or exacerbate the relationship between shocks with individual outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".