Leisure-Related Social Work Interventions for Patients with Cognitive Impairment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The social work profession has been exploring nonpharmacological interventions for patients with cognitive impairment, but there are few evidence-based research outputs. Systematically evaluating the effectiveness of social work interventions for people with cognitive impairment can shed light on the matter to further improve similar interventions. Randomized controlled trials of nonpharmacological interventions for patients with cognitive impairment were selected from key literature databases in both English and Chinese from 2010 to 2021. A systematic review and meta-analysis with Revman 5.4 were performed. Seven trials were included, involving 851 patients with cognitive impairment. The meta-analysis showed that, in terms of overall cognitive function, the Montreal Cognitive Assessment score (MD = 1.64, 95% CI [0.97, 2.30], p < 0.001) of the intervention group was superior to the control group, but there was no significant difference in the Mini-Mental State Examination score between the two groups (MD = 0.33, 95% CI [−0.16, 0.82], p = 0.18). Compared with the control group, nonpharmacological intervention can effectively improve the neuropsychiatric condition of patients (SMD = −0.42, 95% CI [−0.64, −0.20], p = 0.0002). In summary, the current evidence shows that nonpharmacological social work interventions had a positive effect on the cognitive function and neuropsychiatric status of patients with cognitive impairment. Suggestions for future nonpharmacological intervention practice are discussed.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".