Training strategies for an aging workforce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how organizations might support older workers' learning. Design/methodology/approach The paper highlights an incoming HR challenge (training older workers), conducts a review of corporate responses in Europe, and then identifies lessons. Examples are drawn from the case study database of the European Foundation for the Improvement of Living and Working Conditions. Findings The paper identifies four lessons. The first is to adopt a targeted approach, which involves both identifying older employees with key abilities and tailoring training products to their needs. The second lesson is to develop training initiatives that update job‐related skills and knowledge. The third is to complement skills update products with programs that expand the knowledge horizon of older employees. The fourth lesson is to integrate training into recruitment initiatives that target experienced job‐seekers. Originality/value Many organisations are developing initiatives to tap into the older worker talent pool. Training is a critical component of strategies that seek to retain or attract experienced professionals. The paper provides practical advice that will help organizations to design and implement learning programs for older workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it