Harnessing the Potential of Older Workers Through Relationships at Work: Social Support, Feedback, and Performance
Bibliographic record
Abstract
Abstract With the aging of the global workforce, it is crucial to deepen our understanding of how to keep older workers healthy, motivated, and productive. In this research, we integrate job design with socioemotional selectivity theory to propose that social job characteristics relate to employee performance differently for older and younger workers. Specifically, in a 3-wave survey (N = 454), we tested employee age as a moderator of the relationships between receiving social support and feedback at work, and performance, as well as giving social support and feedback at work, and performance. The results showed that, in general, both receiving and giving social support and feedback are associated more strongly with the performance of older than younger workers. The findings provide important theoretical implications for the study of aging and work; they also offer practical applications for creating workplaces in which older workers can reap the benefits of social relationships to remain productive.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".