Older Adults’ Exposure to Ageism in Daily Life – On the Role of TV, Social Media, and Neighborhood Composition
Bibliographic record
Abstract
Abstract Experiences of ageism undermine well-being in old age but the underlying everyday mechanisms are not well understood. Our study focused on three potential exposure contexts: a) TV, b) social media, and c) neighborhood composition that may contribute to the experience of everyday ageism. We aimed to investigate the association between TV viewing, social media use, and neighborhood age composition with ageism reports. We analyzed up to 14 days of end of day diaries from 76 older adults living in Canada (Age: M = 72.01, SD = 9.47; 80% female), with data collection still ongoing. Participants reported their daily TV viewing, social media use, and exposure to ageism each evening over fourteen consecutive days. To determine the age composition of the neighborhoods where participants resided, we used the first three digits of their postal codes. We pre-registered the study on the Open Science Framework. We employed hierarchical linear models to account for the nested data structure. As hypothesized, result indicate that greater overall social media use was significantly associated with greater experiences of ageism in daily life (b = 0.141, p < .01). However, TV viewing and neighborhood age composition showed no significant association with ageism over and above social media use. The results suggest that social media serves as a primary context where older adults experience ageism in their daily lives. Future analyses will replicate the findings using the complete dataset and a larger sample, and will explore potential moderators of the social media–everyday ageism nexus.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".