Effectiveness of Psychological Interventions in Chronic Stage of Stroke: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To examine the effectiveness of interventions for psychological issues faced by individuals post stroke when initiated in the chronic stage of stroke. METHOD: MEDLINE, CINAHL, EMBASE, and Scopus databases were searched from 1980 to July 2012. A study was included if (1) the study was a randomized controlled trial (RCT); (2) at least 50% of individuals in the study were entered into the study at over 6 months post stroke; (3) the study examined the effect of an intervention on psychological functioning; and (4) study participants were ≥ 18 years of age. Similar interventions were grouped and results summarized. Data on the study design, participant characteristics, interventions, outcomes, and adverse events were extracted from each of the selected studies. RESULTS: Nine RCTs met inclusion criteria. All 9 studies examined effectiveness on mood and 3 on adjustment. Repetitive transcranial magnetic stimulation had the strongest evidence of effectiveness in improving mood followed by pharmacotherapy; whereas exercise appeared to be effective in improving adjustment and coping among individuals in the chronic stage of stroke. CONCLUSION: Overall, interventions provided in the chronic stage of stroke appear to be effective in improving mood and adjustment up to 3 months post intervention. The use of multidisciplinary interventions and acceptance models may be important in the overall adjustment process.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".