Psychological distress and vulnerability: Contrasting associations with cognitive performances based on sex and cardiovascular health
Bibliographic record
Abstract
The COVID-19 pandemic has impacted individuals diversely, leading to psychological distress, loneliness and subjective cognitive complaints. People with chronic medical conditions such as cardiovascular diseases (CVD) and those with psychological vulnerabilities were at greater risk of experiencing severe psychological consequences. Women have also reported higher levels of vulnerability and anxiety symptoms compared to men, although this difference appears to lessen with age. This combination of medical conditions, symptoms, and psychological vulnerabilities may influence cognitive aging. Using the COVID-19 pandemic as a universal stressor, this study examines how this combination relates to cognitive health in men and women aged 50 years and over. A total of 122 individuals with stable CVD (87 men, 71.3 %) and 127 healthy individuals (30 men, 23,6 %) completed neuropsychological assessments and questionnaires assessing psychological distress (stress, depression, anxiety symptoms) and psychological vulnerabilities (anxiety sensitivity, rumination, intolerance of uncertainty, and anxious traits). Among healthy participants, men with higher psychological vulnerabilities showed lower global cognitive performance (B = -2.097, p = .002) compared to their female counterparts (B = 0.022, p = .946). Lower psychological distress was associated with better executive functioning performance in individuals with CVD (B = -0.218, p = .019). These findings show evidence that among individuals over 50, psychological distress and vulnerabilities can be associated with lower cognitive performances in executive functioning and global cognitive performance respectively. However, these associations differ according to medical conditions and sex where higher psychological vulnerability among healthy men may act as a risk factor for lower global cognitive health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".