Barriers to Engage Low-Skilled Adults in Educational Opportunities: A Global Perspective
Bibliographic record
Abstract
Abstract The demand for adult education and training (AET) opportunities is substantial as older adults are remaining in the labor force at older ages, and are facing substantial technological changes in the workplace. Strategies to engage middle-aged and older adult workers in AET often exclude low-skilled and sub-populations. The engagement of these sub-populations in AET is challenging as access, awareness, and program costs associated with AET opportunities often target highly skilled populations. The inequality in AET participation warrants specific programs and strategies to address challenges low-skilled adult workers face in pursuing AET. The purpose of this study is to identify AET opportunities for low-skilled middle-aged and older adults, as well as highlight major barriers to engage and retain these sub-population in AET. Data were collected from 36 key informants through semi-structured interviews and through document reviews. Key informants represented Australia, Canada, Italy, Norway, the Netherlands, the U.K., and the U.S. Descriptive methods were used to identify barriers in recruiting and retaining low-skilled middle-aged and older adults. We particularly focused on the barriers related to cost, language, access, and awareness. Results highlighted opportunities tailored to support adult workers in the pursuit of adult learning opportunities both domestically and internationally. Barriers including learning histories, lack of long-term person-centered support, as well as the role of multiple forms of learning, such as formal and informal learning, were identified. Last, we provide recommendations for recruiting and retaining middle-aged and older adult workers in AET programs.
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.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".