Loneliness in the UK during the COVID-19 pandemic: Cross-sectional results from The COVID-19 Psychological Wellbeing Study
Bibliographic record
Abstract
Objectives: Loneliness is a significant public health issue. The COVID-19 pandemic has resulted in lockdown measures limiting social contact. The UK public are worried about the impact of these measures on mental health outcomes. Understanding the prevalence and predictors of loneliness at this time is a priority issue for research. Design: The study employed a cross-sectional online survey design. Method: Baseline data collected between March 23rd and April 24th 2020 from UK adults in the COVID-19 Psychological Wellbeing Study were analysed (N = 1963, 18-87 years, M = 37.11, SD = 12.86, 70% female). Logistic regression analysis was used to look at the influence of sociodemographic, social, health and COVID-19 specific factors on loneliness. Results: The prevalence of loneliness was 27% (530/1963). Risk factors for loneliness were younger age group (OR: 4.67 – 5.31), being separated or divorced (OR: 2.29), meeting clinical criteria for major depression (OR: 1.74), greater emotion regulation difficulties (OR: 1.04), and poor quality sleep due to the COVID-19 crisis (OR: 1.30). Higher levels of social support (OR: 0.92), being married/co-habiting (OR: 0.35) and living with a great number of adults (OR: 0.87) were protective factors. Conclusions: Rates of loneliness during the initial phase of lockdown were high. Risk factors were not specific to the COVID-19 crisis. Findings suggest that supportive interventions to reduce loneliness should prioritise younger people and those with mental health symptoms. Improving emotion regulation and sleep quality may be optimal initial targets to reduce the impact of COVID-19 regulations on mental health outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".