Behaviour assessment tools in long-term care homes in Canada: a survey
Bibliographic record
Abstract
OBJECTIVE: Many people living in long-term care homes (LTCH) experience changes in behaviour termed the behavioural and psychological symptoms of dementia (BPSD). The valid and reliable assessment of BPSD is essential to guide treatment and monitor the effect of interventions. The aim of this study was to identify behavioural assessment tools implemented in LTCH and factors that impact on their use in clinical care. METHODS: We completed an online mixed-design survey of 300 randomly selected Canadian LTCH between September and November 2018. Respondents were asked to report tools used, reasons for use, methods of administration, training/supports available, confidence in use and challenges faced. Survey results were summarized descriptively and the correlation between implementation supports and confidence examined. Free-text responses were analysed qualitatively. RESULTS: Of 300 LTCH invited to participate, 103 completed the survey. Homes reported using a mean 2.2 ± 1.1 (range 0-7) different tools. The two most commonly used tools were the Dementia Observation System (DOS) and Cohen-Mansfield Agitation Inventory (CMAI). Overall confidence in most aspects of tool use was reported to be high, with workload identified as the greatest challenge. Training and supports correlated with confidence in tool use. Qualitative findings indicate tools provide valuable data to understand behaviours, facilitate team communication, target interventions and track outcomes. CONCLUSIONS: Behavioural assessment tools, in particular a direct observation tool, are widely used in clinical care in Canadian LTCH. Education, enhanced resources, leadership support and applications of technology represent opportunities to improve their use.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".