The Impact of Covid-19 on Severe Mentally ill Patients in One Mental Health Center in Kosovo
Bibliographic record
Abstract
Immediately after the onset of the pandemic, some scholars speculated that people with serious mental illnesses would be at uniquely high risk during this period. Recent studies show that people with serious mental illness are at increased risk of being infected by Covid-19 and have higher subsequent rates of hospitalization, morbidity, and mortality.There are studies that also show that stress caused by the Covid-19 pandemic and restrictive measures can precipitate and worsen psychotic symptoms. Our aim was to understand the mental state of the mentally ill people at one Mental Health Center in Prizren, Kosovo as a result of the situation created by Covid-19. It’s a cross-sectional study. 91 patients diagnosed with severe mental illness (Schizophrenia and other psychotic disorders) and 47 their primary caregivers were interviewed via phone calls or directly about their mental state. Findings showed that 15.2% of the sample didn’t use medication regularly while 27% didn’t follow the pandemic rules / restrictions. The level of self-care was not present in about 24.6 % of the sample. Also participants reported the presenceof somatic complaints (26.1%), aggression (23.2%), nervousness (21%), fear (20.3%)and suicidal thoughts (2.9%). Moreover, 13.8% of patients were not in a good moodand 12.3% did not sleep well.Our findings are in line with studies reporting that schizophrenic patients are unimpressed by Covid-19 situation. A quarter to one fifth of patients with severe mental illness showed signs of deterioration. It is difficult to conclude on the extent of their suffering and further studies are needed.Further studies should determine the level and modes of impact of Covid-19 on this vulnerable category of the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".