COVID-19 and the Elderly’s Mental Illness: The Role of Risk Perception, Social Isolation, Loneliness and Ageism
Bibliographic record
Abstract
For almost two years, populations around the globe faced precariousness and uncertainty as a result of the COVID-19 pandemic. Older adults were highly affected by the virus, and the policies meant to protect them have often resulted in ageist stereotypes and discrimination. For example, the public discourse around older adults had a paternalistic tone framing all older adults as "vulnerable". This study aimed to measure the extent to which perceived age discrimination in the context of the COVID-19 pandemic, as well as the sense of loneliness and social isolation, fear and perception of COVID-19 risks, had a negative effect on older adults' mental illness. To do so, a self-report questionnaire was administered to 1301 participants (average age: 77.25 years old, SD = 5.46; 56.10% females, 43.90% males). Descriptive and correlational analyses were performed, along with structural equation modelling. Results showed that perceived age discrimination in the context of the COVID-19 pandemic positively predicts loneliness and also indirectly predicts mental illness. In addition, loneliness is the strongest predictor of mental illness together with fear of COVID-19 and social isolation. Such results highlight the importance of implementing public policies and discourses that are non-discriminating, and that favour the inclusion of older people.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".