The Aggressive Behavior Scale: A New Scale to Measure Aggression Based on the Minimum Data Set
Bibliographic record
Abstract
OBJECTIVES: To examine the reliability and validity of the Aggressive Behavior Scale (ABS), derived from the Minimum Data Set (MDS 2.0). DESIGN: Retrospective analysis of MDS 2.0 and Cohen Mansfield Agitation Inventory (CMAI) data. SETTING: Ontario nursing homes (NHs) and complex continuing care (CCC) hospitals and units. PARTICIPANTS: Two hundred fourteen patients of a CCC hospital, 652 residents of four NH facilities who adopted the MDS 2.0 before its mandatory implementation, 124,259 CCC patients assessed with the MDS 2.0 between July 1996 and October 2006. MEASUREMENTS: In all samples, trained facility clinical staff completed the MDS 2.0 as part of normal clinical practice. The ABS is a 4-item summary scale measuring verbal and physical abuse, socially inappropriate behavior, and resisting care. In the single CCC facility, clinical facility staff completed the CMAI during the same assessment period as the MDS 2.0. RESULTS: Alphas for the ABS were between 0.79 and 0.93 for the three samples. A strong relationship was found between the ABS and the aggressive subscale of the CMAI (correlation coefficient=0.72, P<.001). Impairment in cognition was found to be related to higher ABS scores in all three samples. In CCC, individuals who had higher ABS scores also had a higher prevalence of psychiatric diagnoses and greater frequency of daily restraint use (P<.001 for each dependent variable). CONCLUSION: The ABS provides a useful measure of the severity of aggressive behavior that can be used for care planning, quality measurement, and research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".