The Effectiveness of Multistrategies on Disruptive Vocalization of People With Dementia in Institutions
Bibliographic record
Abstract
The aim of this study was to evaluate the effectiveness of the daily interventions used by the nurses on disruptive vocalization (DV). DV includes all types of disturbing or unacceptable vocal expression: repetitive vocalization, verbal or nonverbal utterances, presented as inappropriate language, repeated and insistent demands, repeated calling out, shouting, complaining, or moaning that does not pertain to their circumstances or environment. A convenience sample of five nursing homes from the north of Italy, in the Friuli Venezia Giulia region, was included in the study. A randomized selection of 87 daily shifts was selected. Institutionalized patients with dementia, but with no associated psychiatric disorders, were eligible. Nurses involved in the study added patients progressively. Nurses involved were asked to keep diaries to record strategies and durations for each episode of DV encountered during the allotted shift. In the total amount of observation time (36,540 minutes), 23.6% (8,653 minutes) of nursing care time involved working with and managing DV patients. The nurses recorded an average of 6.5 (302/46) vocalizations on morning shifts and 7.3 (302/41) during afternoon shifts, with an average duration of about a quarter of an hour each. Managing DV with multistrategies reduces the duration of the DV episode and increases the perceived effectiveness of management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".