COVID-19 and Quarantine, a Catalyst for Ageism
Bibliographic record
Abstract
In February 2021, France had more than 76,000 deaths due to COVID-19 and older adults were heavily affected. Most measures taken to reduce the impact of COVID-19 (quarantine, visit ban in nursing home, etc.) significantly influenced the lives of older adults. Yet they were rarely consulted about their implementation. Exclusion of and discrimination against older adults has been accentuated during the COVID-19 pandemic. While many articles discussing COVID-19 also mention ageism, few actually incorporate the perspectives and opinions of older adults. Our research aims to assess the ageism experienced by older adults during the COVID-19 pandemic. We conducted interviews with older adults (63-92 years, mean age = 76 years) in an urban area of France. Participants reported experiencing more ageism during the COVID-19 pandemic, including hostile and benevolent ageism from older adults' families. Despite reports of experiencing ageist attitudes and behaviors from others, however, older adults also identified positive signs of intergenerational solidarity during this COVID-19 crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".