COVID-19, Social Isolation, and Mental Health Among Older Adults: A Digital Catch-22 (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED One of the most at-risk groups during the COVID-19 crisis is older adults, especially those who live in congregate living settings and seniors’ care facilities, are immune-compromised, and/or have other underlying illnesses. Measures undertaken to contain the spread of the virus are far-reaching, and older adults were among the first groups to experience restrictions on face-to-face contact. Although reducing viral transmission is critical, physical distancing is associated with negative psychosocial implications, such as increased rates of depression and anxiety. Promising evidence suggests that participatory digital co-design, defined as the combination of user-centered design and community engagement models, is associated with increased levels of engagement with mobile technologies among individuals with mental health conditions. The COVID-19 pandemic has highlighted shortcomings of existing technologies and challenges in their uptake and usage; however, strategies such as co-design may be leveraged to address these challenges both in the adaptation of existing technologies and the development of new technologies. By incorporating these strategies, it is hoped that we can offset some of the negative mental health implications for older adults in the context of physical distancing both during and beyond the current pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".