Exploring the Impact of Age-Related COVID-19 Messaging on Internalized Ageism in Older Adulthood
Bibliographic record
Abstract
Abstract Public health messages during the COVID-19 pandemic have indicated a higher risk for older people and/or those who have multiple health conditions. Subsequent societal discourse, however, has at times arguably protested the full protection and treatment of older people from COVID-19, potentially contributing to internalized ageism. To date, how older people interpret age-related pandemic messaging and discourse has not been explored. This study examined older adults’ perspectives of age-related COVID-19 messaging and societal discourse, as well as their perceptions of vulnerability, using a social constructionism framework. Adults age 65 to 89 years participated in semi-structured interviews about their thoughts and experiences with ongoing pandemic-related public messaging. Preliminary analysis suggests that participant perspectives of COVID-19 messaging are situated along a continuum of concern associated with contracting the virus. While some, for example, describe minimal concern, others express being fearful. Individual perceptions of safety appear to be informed, in part, by the presence or absence of an underlying health condition. Individual approaches to media criticism and consumption, personal risk-taking thresholds, financial stability, and social connectedness also appear to influence how the participants perceive pandemic-related messaging. Findings suggest the framing of COVID-19 and pandemic protocols, as well as the media’s sensationalization of age-related issues, can impact older peoples’ perceived vulnerability of contracting the virus. Future research is needed to understand the long-term implications of ongoing pandemic-related messaging on older adults’ experiences of aging, as well as the consequences such messaging could pose to for their health and social behaviors.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".