Age and Career Resilience Through the Lens of Life Course Theory: Examining Individual Mechanisms and Macro‐Level Context Across 28 Countries
Bibliographic record
Abstract
ABSTRACT Career resilience is critical to the world's aging workforce, aiding older workers in adapting to the ever‐evolving nature of work. While ageist stereotypes often depict older workers as less resilient when faced with workplace changes, existing research studies offer conflicting evidence on whether older age hinders or improves career resilience. In response to this conflicting evidence, the present study employs multi‐level data from 6772 employees in 28 countries to examine the age‐career resilience relationships and underlying mechanisms, hence advancing our understanding of career resilience across the life course. By integrating macro‐contextual factors such as the unemployment rate and the culture of education with individual‐level mechanisms such as positive career meaning and career optimism, we provide a comprehensive model explaining how career resilience varies across age groups. Grounded in life course theory, our findings resolve prior inconsistencies in resilience research, contribute to bridging the micro‐macro gap in HRM literature, and challenge existing age‐based stereotypes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".