What would help me stop abusing? The family carer's perspective
Bibliographic record
Abstract
BACKGROUND: A third of family members caring for people with dementia report acting abusively towards them, but there are currently no evidence-based interventions to reduce or prevent such behavior. Family carers who act abusively have not previously been consulted about what may help to reduce abuse. METHOD: We prospectively recruited a consecutive sample of 220 family carers of people with dementia referred to secondary psychiatric services. We asked carers who reported any abusive behavior in the previous three months to select from a list of services and potential interventions those that they thought might help to reduce or prevent this abusive behavior. Carers were also asked to suggest other interventions that might help prevent abuse. RESULTS: 113/115 carers who reported any abusive behavior answered questions about possible interventions. The three most frequently endorsed interventions were: medication to help the care recipient's memory (n = 54; 48.2%); written advice on understanding memory problems and what to do (n = 48; 42.9%) and more information from professionals caring for the person with dementia (n = 45; 40.2%). When asked which interventions were most important, medication to help memory (n = 21; 18.6%), home care (n = 17; 15.0%), residential respite and sitting services (both n = 12; 10.6%) were most frequently endorsed. CONCLUSION: To prevent abuse, family carers prioritized medication for memory, good communication from professionals, written advice on memory problems, home care, residential respite and sitting services. As no interventions to reduce abuse by family carers have yet been formally evaluated, a good starting point may be the expressed wishes of family carers.
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".