The Effect of Psychosocial Factors and Functional Independence on Poststroke Depressive Symptoms: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Stroke is the second leading cause of death and a major cause of serious, long-term disability worldwide. The approximately 15 million people each year who experience stroke are at risk of developing depression. Poststroke depressive symptoms affect one third of survivors of stroke. Patients who develop poststroke depressive symptoms experience decreased functional independence, poor cognitive recovery, decreased quality of life, and increased mortality. Survivors of stroke use social support to deal with stress and defend against the adverse effects of negative stroke outcomes. PURPOSE: This study was designed to examine the influence of perceived social support (emotional and informational, tangible, affectionate, and positive social interaction), stress level, and functional independence on depressive symptoms in survivors of stroke. METHODS: A cross-sectional observational study design in outpatient settings and rehabilitation centers was conducted. A convenience sample of 135 survivors of stroke completed the psychometrically valid instruments. RESULTS: Most of the sample had mild or moderate depressive symptoms (26% and 29%, respectively). The mean score for perceived social support was 77.53 (SD = 21.44) on the Medical Outcomes Study Social Support Survey. A negative association was found between depressive symptoms and the social support total score (r = -.65, p < .01). All of the social support subcategories were negatively associated with depressive symptoms. Hierarchical multiple linear regression showed that social support, stress level, and literacy were associated with depressive symptoms (β = -.31, p < .001; β = .45, p < .001; and β = .16, p = .01, respectively) and partially mediated the association between depressive symptoms and functional independence. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: Poststroke depressive symptoms are common among survivors of stroke. Social support may improve health by protecting these individuals from the negative outcomes of stroke and enhance their recovery. Future research is required to examine how related interventions improve social support in caregivers and reduce depressive symptoms in stroke survivors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".