Reducing verbal agitation in people with dementia: Evaluation of an intervention based on the satisfaction of basic needs
Bibliographic record
Abstract
OBJECTIVES: Positive results have been reported with psychosocial interventions used to reduce verbal agitation (VA) in people with dementia, but there is no clear information regarding the proportion of persons who demonstrate significant behavioural improvement with such treatments. The main objectives of this pilot study are (a) to identify the proportion of persons with dementia who demonstrate significant behavioural improvement with a need-based intervention to reduce VA and (b) to further evaluate the effectiveness of this type of intervention. METHOD: A single-group repeated measures design was used (N = 26). An individualized multicomponent intervention addressing needs for comfort, social interaction and sensory stimulation was applied by a therapist during 30 min sessions during the time of day when VA was most severe. The frequency and duration of VA were measured through computer-assisted direct observation several times before, during and after the intervention. RESULTS: A statistically significant reduction of the duration of VA during the intervention phase relative to other phases of the protocol was found. This effect was limited to the period during which the treatment is being applied. Half of the participants (54%) demonstrated considerable behavioural improvement during the intervention (50% reduction of symptoms) and those whose functioning is best preserved showed the best response to the intervention. CONCLUSION: This need-based intervention appears promising for improving the behaviour of a considerable proportion of patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".