Reducing the use of antipsychotics in dementia care through staff education and family participation
Bibliographic record
Abstract
Background: The widespread use of antipsychotic medication to treat the behavioural and psychological symptoms of dementia in residential aged care facilities is a world wide concern. Despite evidence of the numerous adverse effects of antipsychotic drugs, and the efficacy of non-pharmacological approaches, prescription rates are increasing in aged care. Methods: This controlled before and after study aimed to investigate if an education intervention with family participation in dementia care improved the use of antipsychotic drugs. Antipsychotic use was measured by audit of residents’ clinical records. Three similar rural residential aged care facilities (RACF’s) participated in the study. At site 1 and 2 staff undertook training in dementia care using an on-line learning tool and peer reviewed literature on the use of antipsychotic drugs in dementia. Additionally, family members participated in ‘resident life story telling’ at site 2. Site 3 acted as the control. Results: At sites 1 and 2, twenty five staff (25%) volunteered to participate in training. No training was provided at the control site. Across the three sites 47 residents had a clinical diagnosis of dementia with 30 of this group prescribed antipsychotic medication at baseline. At the intervention sites the use of antipsychotic medication reduced from 85% to 69% at site 1 and from 50% to 38% at site 2. At the control site medication use increased from 61% from 69%. Conclusion: Dementia education for staff, especially with family participation in resident life story telling, may reduce antipsychotic medication use in residential aged care. Additionally, positive clinical implications such as reduction in falls were observed. The encouraging findings of this small study support further investigation in a larger sample.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".