Ageism and Undergraduate Attitudes towards Older Adults during COVID-19
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has shed light on the far reaches of ageism in our society. The current study sought to better understand ageist beliefs in Canadian undergraduate students during the pandemic. As part of a larger survey on ageism, we conducted a thematic analysis on open-ended responses to the following questions: 1) “Has your relationship with older adults in your life changed as a result of the COVID-19 pandemic?” and 2) “Have you noticed that attitudes or opinions towards older adults in your community have changed as a result of the COVID-19 pandemic?” Students felt that older adults should be treated differently during the pandemic because they are seen as “high risk” or “vulnerable.” Furthermore, students felt that they needed to be more cautious around older adults because older adults need to be taken care of. Students expressed fear about transmitting the virus to older adults in their lives so chose to isolate from grandparents or avoid older adults in the community in an effort to keep them safe. Finally, examples of negative and positive ageism were present in responses. Negative ageism was seen in comments about how older adults were going to die anyway, the assumption that older adults need more help, and the belief that older adults should be staying home during the pandemic. Positive ageism was present where students realized the importance of their relationships with the older adults in their lives. Results are discussed using the framework of implicit and explicit 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".