Non-pharmacological interventions for aggressive behavior in older adults living in long-term care facilities
Bibliographic record
Abstract
BACKGROUND: Aggressive behavior (AB) is common in institutional settings. It is an important issue because of its consequences on both the person manifesting such behaviors and their caregivers. Although there are numerous studies assessing non-pharmacologic strategies to manage AB in older adults, no extensive review of the literature is available. This review synthesizes the current knowledge on the effectiveness of non-pharmacological interventions in institutional settings. METHOD: Papers describing the assessment of a non-pharmacological intervention to manage AB in which participants were at least 60 years old and living in a long-term care facility were selected mainly by searching various databases. RESULTS: A total of 41 studies were identified and included in the review. These studies mainly use quasi-experimental designs and include less than 30 participants. Sixty-six percent (27/41) of the studies report either a statistically or behaviorally significant reduction of AB as a result of a non-pharmacological intervention. Staff training programs and environmental modifications appear to be the most effective strategies. CONCLUSION: Non-pharmacological interventions seem effective for managing AB. Future studies on the effectiveness of these interventions need to be more rigorous. Development in this field needs to be based on knowledge regarding the determinants of AB in older adults.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".