Influence of loneliness and social isolation before and during the <scp>COVID</scp>‐19 pandemic on mood, cognition and sleep
Bibliographic record
Abstract
BACKGROUND: Social isolation and loneliness are public health problems and are related to poor physical and mental health in older persons, especially during the COVID-19 pandemic. We investigated the influences of social isolation and loneliness on mood, cognition and sleep quality in older persons. METHODS: This study evaluated 82 older persons, with a median age of 69.16 years (range: 60.00-85.97). The older persons were assessed before and during the period of the COVID-19 pandemic. Cognition was assessed using the Montreal Cognitive Assessment, symptoms of depression using the Beck Depression Inventory II, symptoms of anxiety using the Beck Anxiety Inventory, quality of sleep by the Pittsburgh Sleep Quality Index, daytime sleepiness by the Epworth Sleepiness Scale, isolation by the Duke Social Support Index and three-item UCLA Loneliness Scale. RESULTS: Our results revealed that loneliness is related to worsening anxiety symptoms (P = 0.008), and sleep quality (P = 0.011). Isolation is related to worsening sleep quality (P = 0.011). On the other hand, participants who did not isolate themselves during the pandemic felt more anxious (P = 0.021). In addition, older persons who were not isolated (P = 0.035) and had no loneliness (P = 0.007), have higher cognitive performance over time. CONCLUSION: Loneliness is related to worsening symptoms of anxiety and sleep quality. Our results showed that social isolation is related to worsening sleep quality. On the other hand, high social support during the COVID-19 pandemic increased anxiety. Furthermore, better cognitive performance is related to non-isolated and non-lonely participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".