Nonpharmacological Management of Behavioral and Psychological Symptoms of Dementia: What Works, in What Circumstances, and Why?
Bibliographic record
Abstract
Abstract Objective Behavioral and psychological symptoms of dementia (BPSD) refer to the often distressing, noncognitive symptoms of dementia. BPSD appear in up to 90% of persons with dementia and can cause serious complications. Reducing the use of antipsychotic medications to treat BPSD is an international priority. This review addresses the following questions: What nonpharmacological interventions work to manage BPSD? And, in what circumstances do they work and why? Method A realist review was conducted to identify and explain the interactions among context, mechanism, and outcome. We searched electronic databases for empirical studies that reported a formal evaluation of nonpharmacological interventions to decrease BPSD. Results Seventy-four articles met the inclusion criteria. Three mechanisms emerged as necessary for sustained effective outcomes: the caring environment, care skill development and maintenance, and individualization of care. We offer hypotheses about how different contexts account for the success, failure, or partial success of these mechanisms within the interventions. Discussion Nonpharmacological interventions for BPSD should include consideration of both the physical and the social environment, ongoing education/training and support for care providers, and individualized approaches that promote self-determination and continued opportunities for meaning and purpose for persons with dementia.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".