Older Worker Engagement in the 21st Century as Measured by EENDEED: Who, What, and Why!
Bibliographic record
Abstract
In the evolving landscape of the US workforce, the participation of older adults has garnered increasing attention. While much research has highlighted the engagement of individuals aged 50 and above, a significant gap remains in the literature regarding the engagement of those aged 65 and above. This paper aims to fill this gap by investigating the extent of engagement and the factors that drive engagement among individuals aged 65 and above. Advances in healthcare, shifts in retirement policies, and a desire for continued contribution and engagement play crucial roles in this trend. By examining employment patterns, motivations, and the unique challenges this age group faces, the study aims to highlight the significant yet often overlooked impact of this age group on the modern workplace. The research utilizes the EENDEED measurement instrument to assess engagement levels and explores the theoretical frameworks of self-determination, self-efficacy, and social exchange. The findings reveal that income and gender significantly contribute to engagement levels, while education and years of experience have a limited impact. The study emphasizes the importance of recognizing and leveraging the strengths of older workers to create a more inclusive and productive work environment.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".