Understanding the interplay between social isolation, age, and loneliness during the COVID-19 pandemic
Bibliographic record
Abstract
Previous studies indicate differences in experiences of loneliness during the COVID-19 pandemic but are constricted by limited timeframes and absence of key risk factors. This study explores temporal and inter-individual variations of loneliness in Canadians over the pandemic's first year (April 2020-2021), by identifying loneliness trajectories. It then seeks to provide information about groups overrepresented in high and persistent loneliness trajectories by examining their associations with risk factors: social isolation indicators (living alone, adherence to health measures limiting in-person contacts, and online contacts), young adultood, and the interactions between these factors. Data comes from a large longitudinal study with a representative Canadian sample (n = 1763) and 11 measurement times. Analyses consist of (1) a group-based modelling approach to identify trajectories of loneliness and (2) multinomial logistic regressions to test associations between risk factors and trajectory membership. Varied experiences of loneliness during the pandemic were revealed as five trajectories were identified: moderate-unstable (38.5%), high-stable (26.7%), low-unstable (20.5%), very low-stable (8.6%), and very high-decreasing (5.7%). Individuals living alone associated with higher trajectories. Contrary to our expectations, adhering to social distancing measures and having fewer online contacts associated with lower trajectories. Age and interactions were not significant in regard to loneliness trajectories.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 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".