Fear of COVID-19 is associated with trust, subjective numeracy, and differentially with loneliness in older versus younger adults
Bibliographic record
Abstract
Introduction The emotional impacts of the COVID-19 pandemic and resulting public health emergency are only beginning to be understood. Methods We assessed the contributions of emotional and cognitive factors and age-related comorbidities to greater COVID-19 fear in a community dwelling sample of 142 younger (Mage = 19.63, SDage = 2.59) and 157 older (Mage = 72.01, SDage = 7.06) adults, between July 2020 and July 2021. We hypothesized that individuals with increased loneliness, depression, and/or decreased subjective numeracy (SN) and interpersonal trust would experience more COVID-19 fear. We also predicted that females and older adults would experience more COVID-19 fear given that age-related comorbidities are associated with increased illness severity. Results Results showed that the extent of loneliness in older adults was more strongly related to fear of COVID-19 than it was in younger adults (β = 0.197, p = 0.016), and poorer SN was associated with increased COVID-19 fear in both age groups (β = −0.138, p = 0.016). Further, higher interpersonal mistrust was associated with increased COVID-19 fear (β = 0.136, p = 0.039), as was identifying as female (β = 0.137, p = 0.013). Discussion Given that self-described poor numeracy was a marker for greater COVID-19 fear, investigators and policy makers might consider mitigation opportunities addressing data literacy requirements imposed by the media. Further, outreach to mitigate loneliness, particularly of the elderly, might effectively lessen the negative psychological impact of this ongoing public health crisis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".