The burden of loneliness: Implications of the social determinants of health during COVID-19
Bibliographic record
Abstract
This study sought to examine if mental health issues, namely depression and anxiety symptoms, and loneliness were experienced differently according to various demographic groups during the COVID-19 pandemic (i.e., a societal stressor). An online survey, comprising demographic questions and questionnaires on depression, anxiety and loneliness symptoms, was distributed in Canada during the height of social distancing restrictions during the COVID-19 pandemic. Respondents (N=661) from lower income households experienced greater anxiety, depression and loneliness. Specifically, loneliness was greater in those with an annual income <$50,000/yr versus higher income brackets. Younger females (18-29yr) displayed greater anxiety, depressive symptoms and loneliness than their male counterparts; this difference did not exist among the other age groups (30-64yr, >65yr). Moreover, loneliness scores increased with increasing depression and anxiety symptom severity category. The relationship between loneliness and depression symptoms was moderated by gender, such that females experienced higher depressive symptoms when encountering greater loneliness. These data identify younger females, individuals with lower income, and those living alone as experiencing greater loneliness and mental health challenges during the height of the pandemic in Canada. We highlight the strong relationship between loneliness, depression and anxiety, and emphasize increased vulnerability among certain cohorts.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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".