Never too late to learn: Unlocking the potential of aging workforce in manufacturing and service industries
Bibliographic record
Abstract
This study systematically reviews 51 articles from Scopus and Web of Science databases to investigate the learning of aging workers in the manufacturing and service industries. It focuses on three key research questions: factors influencing learning among aging workers, effective learning approaches for this demographic, and strategies for enhancing their learning outcomes. The factors influencing learning were categorized into individual, organizational, and societal dimensions, illustrating the sophisticated interaction that shapes the learning environment. Effective learning approaches identified include lifelong learning, utilizing technology, and intergenerational learning, which are interrelated and reinforce each other. Furthermore, we propose a seven-step socio-technical system approach to enhance learning for the aging workforce. This novel approach considers technological tools, as well as human, organizational, and societal elements that play an essential role in the learning process. Our findings present a comprehensive perspective on the complexities of older workers' learning and offer actionable insights to enhance their learning experience. The proposed socio-technical model contributes to creating an inclusive and supportive learning environment, aiming to boost key areas, such as job performance, satisfaction, health, and well-being. This study's implications extend to organizations aiming to optimize the potential of an aging workforce in a rapidly evolving digital world.
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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 | 0.001 | 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 | 0.001 |
| 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".