Pattern and associated factors of cognitive failures in the general chinese population during the early stages of the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Background: The coronavirus disease-2019 (COVID-19) pandemic worldwide has caused a high burden of mental problems, which may be associated with subjective cognitive impairment in the general population. Objectives: This cross-sectional survey aimed to investigate the level and pattern of cognitive failure (CF) and its negative changes during the COVID-19 pandemic among the general population in China. Methods: The participants completed an online questionnaire between April 18 and May 4, 2020, and those aged between 18 and 70 were included in this study. CF was measured using the 14-item CF Questionnaire (CFQ-14). Factors associated with CF and negative changes in CF were evaluated using multiple linear and logistic regression models. A total of 30,879 eligible participants were recruited; most were female (59.10%) and aged 31–45 (61.51%). Results: The mean CFQ-14 score was 15.62 (standard deviation = 11.55), and 4,619 (14.96%) participants reported negative changes in CF during the pandemic. Multiple regression analyses showed that participants with female gender, history of physical and mental disease, the self-perceived influence of COVID-19, altered appetite and taste preference, worse interpersonal relationships, long sleep duration, poor sleep quality, depressive, anxiety and posttraumatic stress disorder symptoms had a higher level of CF and negative CF changes, while regular exercise was associated with a lower risk of both outcomes. Conclusions: This study indicates that CF symptoms should be monitored in the general population during pandemics. A healthy lifestyle and reduction in psychological stress could help promote normal cognitive function during pandemics.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".