A TRAINING PROGRAM FOR Managing Agitation of Residents in Long-Term Care Facilities: DESCRIPTION AND PRELIMINARY FINDINGS
Bibliographic record
Abstract
The purpose of this article is to describe a training program for managing agitation of long-term care residents and to report results of a pilot study. The program emphasizes the development of behavioral skills for the assessment, prevention, and reduction of both aggressive and non-aggressive agitated behavior. It includes 8 hours of class instruction followed by 8 hours of weekly supervision by the trainers. The nursing staff of all working shifts of a unit located in a large nursing home and the residents of this unit participated in the pilot study. Residents and staff were assessed prior to and after the 2-month training program. Staff members reported using behavioral techniques to a greater extent and feeling more effective in managing agitation after training. More than 90% of staff members were satisfied with the training program. During the supervision period, the staff developed and implemented individualized interventions for two residents. The interventions involved providing more attention to these residents and, in one case, modifying some aspects of the direct environment which seemed to trigger agitation. Both residents were less agitated after the interventions were implemented. Moreover, there was a reduction in the number and frequency of agitated behaviors for the other residents of the trainees' unit following staff training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".