Identification and Treatment of Agitation in Nursing Home Settings: Applying the IPA Algorithm
Bibliographic record
Abstract
Abstract Background With an estimated prevalence of 55%, agitation is particularly common among nursing home residents. The recent International Psychogeriatric Association (IPA) agitation treatment algorithm describes steps for the identification and treatment of agitation across multiple care settings. Method This abstract describes the application of the IPA algorithm to the long‐term care (LTC) setting. Result In the IPA algorithm, nonpharmacologic care is considered first and continued for both treatment and prevention. In LTC residents, nonpharmacologic interventions are a key first step. For agitation in nursing home residents with dementia, the current evidence suggests that group activity based interventions (e.g., recreation therapy), resident interventions (e.g., massage and touch therapy, and music therapy) and multidisciplinary training and care (e.g., person‐centred care) are the most consistently effective nonpharmacologic treatments. There are several caveats that may impact efficacy of interventions. Feasibility and scalability considerations may impact implementation of nonpharmacologic interventions in long‐term care settings. Most interventions require training, implementation, or supervision by external specialized staff. Feasibility of interventions may also be limited by the capabilities of individuals with dementia; sensory impairments and physical disabilities may preclude some residents from participating in interventions such as music therapy or exercise. As such, interventions must be personalized. Data also show that nonpharmacologic interventions that modify the environment or stimulate the senses may be effective for different aspects of agitation in LTC residents with dementia. Pharmacologic care is personalized and guided by the major features of the agitation including when it occurs, severity and whether it represents a danger to self or others. Importantly, recent efforts have evaluated multi‐modal interventions in LTC. Taken in toto, literature to date provides evidence for use of the above mentioned nonpharmacological interventions for mild agitation in long‐term care facilities. Implementing these interventions requires assessment of medical and environmental causes for agitation, ongoing nonpharmacologic intervention and then pharmacologic interventions as necessary. Conclusion Future studies should continue to investigate the long‐term effects of multi‐modal nonpharmacological interventions, alone and in combination with pharmacologic interventions in participants with severe agitation.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".