Return to work after mild-to-moderate stroke: work satisfaction and predictive factors
Bibliographic record
Abstract
A large proportion of stroke patients are unable to return to work (RTW), although figures vary greatly. A total of 121 mild-to-moderate stroke patients, who had a paid job at the time of their stroke were included (a) to quantify RTW and work satisfaction one-year post-stroke (using the Utrecht Scale for Evaluation of Rehabilitation-Participation) and (b) to determine factors predicting RTW post-stroke, based on stroke-related, personal and neuropsychological variables. Half of the patients were not in work (28%) or were working less (22%) than pre-stroke. Ninety percent of those in fulltime employment post-stroke were satisfied with their occupational situation, against 36% of the unemployed participants. In regards to factors predicting RTW, global cognitive functioning (r = .19, Montreal Cognitive Assessment) and depressive symptoms (r = -.16, Hospital Anxiety and Depression Scale) at two months post-stroke onset were associated with return to work within one year. Only global cognitive functioning was an independent predictor of RTW (11.3% variance, p = .013). Although the explained variance was not that high, neuropsychological factors probably play a pivotal role in returning to work and should be taken into account during rehabilitation after mild and moderate stroke.
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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 | 0.000 |
| 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".