Relationship Between Psychological Impacts of COVID-19 and Loneliness in Patients With Dementia: A Cross-Sectional Study From Iran
Bibliographic record
Abstract
Objectives: Although the COVID-19 pandemic has affected people all around the world, the elderly is at a higher risk of suffering from its consequences. One of the serious concerns is developing loneliness and post-traumatic stress symptoms, which may contribute to cognitive decline at older ages. This study aimed to examine the psychological responses and loneliness in elderly patients diagnosed with dementia. Methods: Twenty-one patients diagnosed with dementia, with ages older than 40, and 19 caregivers were enrolled in the study. The patients have undergone a comprehensive neuropsychiatric interview and were assessed with De Jong Gierveld Scale for loneliness and Impact of Event Scale-Revised (IES-R). The severity of dementia was assessed by Functional Assessment Staging Tool (FAST Scale) and the Montreal Cognitive Assessment (MoCA). Results: -value: 0.046). There was a negatively significant correlation between MoCA score and avoidance. Hyperarousal was significantly correlated with emotional loneliness in patients. Conclusion: We found a direct relationship between cognitive decline and the psychological impacts of COVID-19. Our results highlight the need for more comprehensive studies to further investigate the influence of the pandemic on the worsening of cognitive impairment and loneliness in patients with dementia.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".