Assessment of agitation in elderly patients with dementia: correlations between informant rating and direct observation
Bibliographic record
Abstract
BACKGROUND: Assessment of behavior problems in elderly persons with dementia is important for understanding and managing those behaviors. The most common method for assessing agitation is the use of informant ratings; however, these ratings may be affected by staff bias, inaccurate or insufficient memory, or stress. An alternative method is direct observation, which is more objective, but very costly and necessitates time sampling, thereby limiting the period covered by the assessment. To date, little research attention has been given to the degree to which these two methods converge. METHODS: In the present study, 175 elderly persons with dementia who manifested problem behaviors were recruited from 11 nursing home facilities in Maryland. The average age for the participants was 87 years; 78% were female. Two methods were employed for assessing agitation: the Agitated Behaviors Mapping Instrument (ABMI), which is based upon direct observations, and the Cohen-Mansfield Agitation Inventory (CMAI), which is a frequency rating scale completed by a formal caregiver. The ABMI and CMAI contain some identical items for tapping behavior problems. RESULTS: Data analysis revealed significant Pearson correlations between identical items on the two assessment instruments, as well as significant correlations of summary measures based on these different instruments, demonstrating a strong convergence between informant ratings and direct observations. CONCLUSIONS: Informant ratings can achieve moderate agreement with direct observation when valid instruments and informants are used.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".