Ageism and the Older Worker: A Scoping Review
Bibliographic record
Abstract
PURPOSE OF THE STUDY: Given the policy shifts toward extended work lives, it is critically important to address barriers that older workers may face in attaining and maintaining satisfactory work. This article presents a scoping review of research addressing ageism and its implications for the employment experiences and opportunities of older workers. DESIGN AND METHODS: The five-step scoping review process outlined by Arksey and O'Malley was followed. The data set included 43 research articles. RESULTS: The majority of articles were cross-sectional quantitative surveys, and various types of study participants (older workers, human resource personnel/manager, employers, younger workers, undergraduate students) were included. Four main themes, representing key research emphases, were identified: stereotypes and perceptions of older workers; intended behavior toward older workers; reported behavior toward older workers; and older workers' negotiation of ageism. IMPLICATIONS: Existing research provides a foundational evidence base for the existence of ageist stereotypes and perceptions about older workers and has begun to demonstrate implications in relation to intended behaviors and, to a lesser extent, actual behaviors toward older workers. A few studies have explored how aging workers attempt to negotiate ageism. Further research that extends beyond cross-sectional surveys is required to achieve more complex understandings of the implications of ageism and inform policies and practices that work against ageism.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".