Education Level Modulates the Presence of Poststroke Depression and Anxiety, But It Depends on Age
Bibliographic record
Abstract
ABSTRACT: Depression and anxiety are common complications after stroke and little is known about the modulatory roles of education and age. Our study aimed to evaluate the modulatory effects of education level on anxiety and depression after stroke and their effect on each age group. Adults with first stroke took part in this cross-sectional observational clinical study. We used the following instruments: Hospital Anxiety and Depression Scale (HADS), Montreal Cognitive Scale, Pittsburgh Sleep Quality Index, Barthel index, and Functional Independence Measure. There were 89 patients. The mean (SD) age was 58.01 (13) years, mean (SD) years of education was 9.91 (5.22), 55.1% presented depression symptoms and 47.2% anxiety symptoms, 56.2% were young adults and 43.8% were older adults. We identified a negative association between education and anxiety score ( r = -0. 269, p = 0.011) and depression score ( r = -0.252, p = 0.017). In the linear regression analysis, we found that education is negatively associated with HADS, but this influence was more consistent in young adults. In conclusion, a higher education level reduces the risk of depression and anxiety, but their effect is less consistent in older adults.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".