Overlooked and Underestimated: Experiences of Ageism in Young, Middle-Aged, and Older Adults
Bibliographic record
Abstract
OBJECTIVES: Although the prevalence of ageism against older people has been well-established, less is known about the characteristics of those experiences or the experiences of young and middle-aged adults. The present study addressed these gaps by examining young, middle-aged, and older adults' self-reports of an ageist action they experienced. METHODS: Participants' descriptions were coded for the domain in which the ageist experience occurred, the perpetrator of the ageist experience, and the type of ageist experience. RESULTS: Young adults most commonly reported experiencing ageism in the workplace with coworkers as perpetrators. Middle-aged and older adults also reported ageism in the workplace; however, they also frequently reported experiencing ageism while seeking goods and services. Perpetrators of ageism varied more widely for middle-aged and older adults. Regardless of one's age, ageism was commonly experienced in the form of a lack of respect or incorrect assumptions. DISCUSSION: The findings enhance our understanding of ageism across adulthood by considering the domains, perpetrators, and types of ageist expressions that adults of all ages encounter. They also suggest that interventions to reduce age bias will require multifaceted approaches that take into account the different forms that individuals experience across the life span.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".