Challenging Behaviors in Nursing Homes: Impact of Staff Training on Laypeople's Perceptions of Staff Competence
Bibliographic record
Abstract
PURPOSE: Challenging behaviors of residents with dementia represent a major concern in nursing homes (NHs). Although various studies have investigated the effects of staff training in challenging behavior management, few have explored their impact on laypeople's perceptions. However, NH professionals routinely interact with laypeople, such as current and prospective residents and their families, volunteers, and community partners. The current study examined the effect of person-centered staff training on naïve individuals' perceptions of staff competence. METHOD: Twenty-two NH professionals completed person-centered care training in challenging behavior management and responded to a clinical vignette before and after training. Their responses were evaluated by 59 naïve assessors for relevance, confidence, and competence. In addition, two trained assessors were recruited to explore whether their informed evaluations corroborated naïve assessors' impressions. RESULTS: Relevance, confidence, and competence improved significantly according to naïve and trained assessors. NH professionals also reported feeling more competent. CONCLUSION: Multiple measures indicate improved staff performance following training. Future studies should examine how laypeople's perceptions of staff competence influence their experiences in NHs and impact NH attractiveness.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".