JOB AVAILABILITY FOR OLDER WORKERS IN A COMMUNITY: ANALYSIS ON JOB POSTINGS
Bibliographic record
Abstract
Abstract Objectives This study examined the current job availability in a local job market for older workers and investigated the required skills the older workers to be competitive. Background Most studies approached older workers’ employment transition from the supply side of the labor market, examining individual-level factors for a successful employment transition. However, there is a lack of knowledge on the demand side, such as job opportunities, regarding older workers’ employment transition. Methods The study uses combined data with job postings collected in the 4th quarter of 2021 from three engines and the O*NET database with occupational information to identify job opportunities and required skills. Additionally, the 2020 and 2021 American Community Survey were used to explore the characteristics of the older workforce in the community. Results A total of 5,160 job postings divided into 468 occupations were found in the community. The most frequently available jobs were healthcare (14%), transportation and material moving (13 %), and sales (13%) occupations, which required some work experience needed. The older labor force mostly had a college or associate degree (45.8%) and was in educational (16%), office (11%), and sales (11%) occupations. All occupations require technological skills. Conclusion The study found a mismatch between the available jobs and the actual older labor force, and those who are in employment transition, even downward movement, would be required some related experience. These suggest that a pre-employment transition program providing job training and age-friendly employment support services would be helpful for older workers in all occupational classes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".