Burnout in Stroke Patients Pre-and Post-COVID-19 Pandemic
Bibliographic record
Abstract
Background: Since the first appearance of COVID-19 numerous complications have been reported particularly within 1.48% of the population suffering from stroke. Maintaining positive mental health is crucial to modulate COVID-19 impacts involving burnout, depression, and anxiety. Objective: The aim of the current study was to investigate the consequences of the COVID-19 pandemic in Egypt on the level of burnout in stroke cases. Patients and methods: A total of 100 Egyptian stroke male and female cases participated in the study. Participants aged between 34 and 70 years old and their cognition score was >26 according to Montreal Cognitive Assessment (MOCA) scale. The Malach-Pines tool was used to measure burnout. Results: The mean scores of all items of the burnout scale increased significantly post COVID-19 in comparison with that of pre COVID-19 (P<0.001). The highest score was for “I've had it” with a mean score of 4.17 (SD 1.08) pre COVID-19, which increased significantly post COVID-19 to 5.98 (SD 0.97). The score of “I've had it” also increased significantly post COVID-19 in both age classes, duration of illness classes, and in females and males (P<0.001), also increase significantly post COVID-19 compared with that pre COVID-19 in subjects with high, medium, and low educational levels (P<0.001). Conclusion: Lockdown procedures related to the COVID-19 pandemic had a major impact on stroke cases whose post COVID-19 burnout levels had increased and led to worse management 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".