Investigating cognitive impairment, biopsychosocial barriers, and predictors of return to daily life among older stroke survivors
Bibliographic record
Abstract
Purpose The aim was to investigate the associations between cognitive impairment and biopsychosocial factors among older stroke survivors and predictors of poststroke return to daily life. Materials and methods This cross-sectional study involved 117 stroke survivors (61% men) with an average age of 77 years (range 65–91). The participants completed two questionnaires (Riksstroke and Short Form 36 questionnaires). The Montreal Cognitive Assessment (MoCA) was used to assess cognitive abilities. The International Classification of Functioning, Disability, and Health (ICF) framework guided the selection of biopsychosocial variables. We used Spearman’s correlation coefficient and multiple logistic regression in the analyses. Results The average MoCA score was 21.7 points (range: 4–30, SD 5.6). The need for assistance from relatives and professionals, need for help with dressing and household chores, reliance on others for mobility, and reading and balance problems were correlated with more severe cognitive impairment (r = 0.20–0.33). Cognitive impairment, fatigue, and balance issues predicted an unfavorable return to daily life (odds ratio: 6.2–6.8). Conclusion The study indicated that cognitive impairment is associated with difficulties in all ICF domains. Cognitive impairment, fatigue, and balance issues are associated with an unsuccessful return to daily life. Prioritizing these factors and screening for cognitive impairment with objective assessment tools may improve rehabilitation outcomes and enhance overall quality of life poststroke.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".