AN INTERNATIONAL COMPARISON OF PARTICIPATION IN LIFELONG LEARNING AND PROBLEM SOLVING ABILITIES FOR OLDER WORKERS
Bibliographic record
Abstract
For several decades, discussions of lifelong learning have focused heavily on adult education and training (AET) opportunities that provide economic benefits, especially those that help learners maintain or gain job-related skills or competencies so they can remain in the labor force at older ages. This can be especially important for older adult workers, who are at risk for experiencing skills obsolescence, as well as receiving fewer work-related training opportunities than younger workers. Using data from the Program for the International Assessment of Adult Competencies (PIAAC), we examined the relationship between participation in formal and non-formal AET, foundational computer skills (ICT), and problem solving abilities in technology-rich environments (PSTRE) for adults between ages 45–65. U.S. data were compared with data from Canada, Germany, and Japan. The U.S. had comparable levels of participation in AET to comparison countries for employed individuals 45–54 (63%), and the highest rates of participation among adults age 55–65 (59%). Even with these high rates of engagement in AET, employed adults in the U.S. between ages 45–54 scored significantly lower in PSTRE than individuals in comparison countries. Despite these scores, 93% of employed U.S. adults 45–54 said they have the ICT skills necessary for their current job, as did 88% of adults 55–65. The relationship between AET, technology skills in the workforce, and the knowledge economy will be discussed in this presentation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".