Psychosocial interventions for dementia patients in long-term care
Bibliographic record
Abstract
BACKGROUND: Psychosocial interventions in long-term care have the potential to improve the quality of care and quality of life of persons with dementia. Our aim is to explore the evidence and consensus on psychosocial interventions for persons with dementia in long-term care. METHODS: This study comprises an appraisal of research reviews and of European, U.S. and Canadian dementia guidelines. RESULTS: Twenty-eight reviews related to long-term care psychosocial interventions were selected. Behavioral management techniques (such as behavior therapy), cognitive stimulation, and physical activities (such as walking) were shown positively to affect behavior or physical condition, or to reduce depression. There are many other promising interventions, but methodological weaknesses did not allow conclusions to be drawn. The consensus presented in the guidelines emphasized the importance of care tailored to the needs and capabilities of persons with dementia and consideration of the individual's life context. CONCLUSIONS: Long-term care offers the possibility for planned care through individualized care plans, and consideration of the needs of persons with dementia and the individual life context. While using recommendations based on evidence and consensus is important to shape future long-term care, further well-designed research is needed on psychosocial interventions in long-term care to strengthen the evidence base for such care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".