Mitigating Social Isolation Following the COVID-19 Pandemic: Remedy Messages Shared by Older People
Bibliographic record
Abstract
At the beginning of July 2022, when public health restrictions were lifted, we deployed a country-wide e-survey about how older people were managing now after COVID-19 pandemic-related anxiety. Our responder sample was stratified by age, sex, and education to approximate the Canadian population. E-survey responders were asked to share open-text messages about what contemporaries could do to live less socially isolated lives at this tenuous turning point following the pandemic as the COVID-19 virus still lingered. Contracting COVID-19 enhanced older Canadians’ risk for being hospitalized and/or mortality risk. Messages were shared by 1189 of our 1327 e-survey responders. Content analysis revealed the following four calls to action: (1) cultivating community; (2) making room for what is good; (3) not letting your guard down; and (4) voicing out challenges. Responders with no chronic illnesses were more likely to endorse making room for what is good. Those with no diploma, degree, or certificate least frequently instructed others to not let their guard down. While COVID-19 is no longer a major public health risk, a worrisome proportion of older people across the globe are still living socially isolated. We encourage health and social care practitioners and older people to share messages identified in this study with more isolated persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".